R Notebook
library(ggeffects)
library(dplyr)##
## Attaching package: 'dplyr'## The following objects are masked from 'package:stats':
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## filter, lag## The following objects are masked from 'package:base':
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## intersect, setdiff, setequal, unionlibrary(ggpubr)## Loading required package: ggplot2library(grid)
library(lmerTest)## Loading required package: lme4## Loading required package: Matrix## Warning: package 'Matrix' was built under R version 4.0.5## Registered S3 methods overwritten by 'lme4':
## method from
## cooks.distance.influence.merMod car
## influence.merMod car
## dfbeta.influence.merMod car
## dfbetas.influence.merMod car##
## Attaching package: 'lmerTest'## The following object is masked from 'package:lme4':
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## lmer## The following object is masked from 'package:stats':
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## steplibrary(devtools)## Loading required package: usethis## Warning: Can't find generic `testthat_print` in package testthat to register S3 method.
## Can't find generic `testthat_print` in package testthat to register S3 method.
## Can't find generic `testthat_print` in package testthat to register S3 method.
## ℹ This message is only shown to developers using devtools.
## ℹ Do you need to update testthat to the latest version?source_url("https://raw.githubusercontent.com/JacobElder/MiscellaneousR/master/corToOne.R")## SHA-1 hash of file is 07e3c11d2838efe15b1a6baf5ba2694da3f28cb1source_url("https://raw.githubusercontent.com/JacobElder/MiscellaneousR/master/plotCommAxes.R")## SHA-1 hash of file is 374a4de7fec345d21628a52c0ed0e4f2c389df8efullLong1 <- data.table::fread("~/Google Drive/Volumes/Research Project/Identities to Traits/Study 1/Cleaning/Output/id2traitDf.csv")
orderDf1 <- data.table::fread( "~/Google Drive/Volumes/Research Project/Identities to Traits/Study 1/Cleaning/Output/orderDf.csv")
idShort1 <- data.table::fread( "~/Google Drive/Volumes/Research Project/Identities to Traits/Study 1/Cleaning/Output/id2traitShort.csv")
indDiff1 <- data.table::fread( "~/Google Drive/Volumes/Research Project/Identities to Traits/Study 1/Cleaning/Output/indDiff.csv")
idSim1 <- data.table::fread( "~/Google Drive/Volumes/Research Project/Identities to Traits/Study 1/Cleaning/Output/identitySimDf.csv")fullLong2 <- data.table::fread("~/Google Drive/Volumes/Research Project/Identities to Traits/Study 2/Cleaning/Output/id2traitDf.csv")
orderDf2 <- data.table::fread( "~/Google Drive/Volumes/Research Project/Identities to Traits/Study 2/Cleaning/Output/orderDf.csv")
idShort2 <- data.table::fread( "~/Google Drive/Volumes/Research Project/Identities to Traits/Study 2/Cleaning/Output/id2traitShort.csv")
indDiff2 <- data.table::fread( "~/Google Drive/Volumes/Research Project/Identities to Traits/Study 2/Cleaning/Output/indDiff.csv")
idSim2 <- data.table::fread( "~/Google Drive/Volumes/Research Project/Identities to Traits/Study 2/Cleaning/Output/identitySimDf.csv")# subset data for traits to only appear once per subject
traitsPerS1 <- fullLong1 %>% distinct(subID, Idx, .keep_all = TRUE)
traitsPerS2 <- fullLong2 %>% distinct(subID, Idx, .keep_all = TRUE)
# subset data for only connected traits to appear per subject
connectDf1 <- fullLong1 %>% filter(connect==1)
connectDf2 <- fullLong2 %>% filter(connect==1)
# convert to factors
fullLong1$connect <- as.factor(fullLong1$connect)
levels(fullLong1$connect) <- list(No = "0", Yes = "1")
fullLong2$connect <- as.factor(fullLong2$connect)
levels(fullLong2$connect) <- list(No = "0", Yes = "1")
# pos neg asymmetry
idShort1$pndiff <- idShort1$pI2Tdeg - idShort1$nI2Tdeg
idShort2$pndiff <- idShort2$pI2Tdeg - idShort2$nI2TdegIdentity Typicality
Traits that are nominated as typical of some identity are evaluated more self-descriptively
Study 1
connect1 <- lmer(scale(selfResp) ~ connect + scale(subTend) + scale(traitTend) + ( connect | subID ) + ( 1 | traits), data=fullLong1)
summary(connect1)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(selfResp) ~ connect + scale(subTend) + scale(traitTend) +
## (connect | subID) + (1 | traits)
## Data: fullLong1
##
## REML criterion at convergence: 1399366
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.2593 -0.6864 -0.0184 0.6681 3.7715
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## traits (Intercept) 0.14191 0.3767
## subID (Intercept) 0.04058 0.2014
## connectYes 0.05296 0.2301 -0.26
## Residual 0.64345 0.8022
## Number of obs: 582288, groups: traits, 296; subID, 246
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -0.01503 0.02541 463.81941 -0.591 0.554
## connectYes 0.18925 0.01554 249.85050 12.181 <2e-16 ***
## scale(subTend) 0.01563 0.01246 243.17929 1.255 0.211
## scale(traitTend) 0.40080 0.02192 294.05314 18.283 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) cnnctY scl(sT)
## connectYes -0.129
## scal(sbTnd) 0.000 -0.015
## scl(trtTnd) 0.000 -0.003 0.000connect1.plot <- ggpredict(connect1, c("connect")) %>% plot(show.title=FALSE) + jtools::theme_apa() + xlab("Identity-Typicality") + ylab("Self-Evaluation")
connect1.plot
Study 2
connect2 <- lmer(scale(selfResp) ~ connect + subTend + traitTend + ( connect | subID ) + ( 1 | traits), data=fullLong2)
summary(connect2)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(selfResp) ~ connect + subTend + traitTend + (connect |
## subID) + (1 | traits)
## Data: fullLong2
##
## REML criterion at convergence: 1407917
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.8388 -0.6677 -0.0131 0.6664 4.1796
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## traits (Intercept) 0.16014 0.4002
## subID (Intercept) 0.06177 0.2485
## connectYes 0.03923 0.1981 -0.33
## Residual 0.63392 0.7962
## Number of obs: 589488, groups: traits, 296; subID, 249
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -6.079e-01 4.866e-02 4.350e+02 -12.492 <2e-16 ***
## connectYes 1.664e-01 1.356e-02 2.451e+02 12.271 <2e-16 ***
## subTend 7.096e-04 6.751e-04 2.439e+02 1.051 0.294
## traitTend 9.511e-01 5.995e-02 2.940e+02 15.866 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) cnnctY subTnd
## connectYes -0.092
## subTend -0.322 -0.024
## traitTend -0.750 -0.003 0.000connect2.plot <- ggpredict(connect2, c("connect")) %>% plot(show.title=FALSE) + jtools::theme_apa() + xlab("Identity-Typicality") + ylab("Self-Evaluation")
connect2.plot
Combined
plotCommAxes(connect1.plot, connect2.plot, "Connect", "Self-Evaluation")
Effect of Identity Typicality Depends on Identity Importance
Study 1
Identity importance defined by strength of identification. This is not significant for identity-to-identity centrality.
connect.streng1 <- lmer(scale(selfResp) ~ connect * scale(streng) + subTend + traitTend + ( connect + scale(streng) | subID ) + ( 1 | traits), data=fullLong1)## boundary (singular) fit: see help('isSingular')summary(connect.streng1)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(selfResp) ~ connect * scale(streng) + subTend + traitTend +
## (connect + scale(streng) | subID) + (1 | traits)
## Data: fullLong1
##
## REML criterion at convergence: 1399343
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.2607 -0.6863 -0.0183 0.6682 3.7875
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## traits (Intercept) 1.420e-01 0.376825
## subID (Intercept) 4.056e-02 0.201385
## connectYes 5.157e-02 0.227097 -0.26
## scale(streng) 1.648e-05 0.004059 0.17 -1.00
## Residual 6.434e-01 0.802125
## Number of obs: 582288, groups: traits, 296; subID, 246
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -6.642e-01 4.493e-02 4.035e+02 -14.783 < 2e-16 ***
## connectYes 1.860e-01 1.536e-02 2.503e+02 12.109 < 2e-16 ***
## scale(streng) -4.934e-03 1.294e-03 2.805e+03 -3.813 0.00014 ***
## subTend 6.173e-04 5.130e-04 2.433e+02 1.203 0.23005
## traitTend 9.622e-01 5.265e-02 2.937e+02 18.275 < 2e-16 ***
## connectYes:scale(streng) 2.740e-02 4.858e-03 2.185e+05 5.640 1.7e-08 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) cnnctY scl(s) subTnd trtTnd
## connectYes -0.065
## scal(strng) 0.008 -0.188
## subTend -0.290 -0.015 0.006
## traitTend -0.772 -0.003 0.001 0.000
## cnnctYs:s() 0.001 -0.039 -0.240 -0.004 0.000
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')connect.streng1.plot <- ggpredict(connect.streng1, c("streng","connect")) %>% plot(show.title=FALSE) + jtools::theme_apa() + xlab("Strength of Identification") + ylab("Self-Evaluation")
connect.streng1.plot
Study 2
connect.streng2 <- lmer(scale(selfResp) ~ connect * scale(streng) + ( connect + scale(streng) | subID ) + ( 1 | traits), data=fullLong2)## boundary (singular) fit: see help('isSingular')summary(connect.streng2)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula:
## scale(selfResp) ~ connect * scale(streng) + (connect + scale(streng) |
## subID) + (1 | traits)
## Data: fullLong2
##
## REML criterion at convergence: 1408086
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.8410 -0.6676 -0.0133 0.6669 4.1877
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## traits (Intercept) 2.966e-01 0.5446027
## subID (Intercept) 6.202e-02 0.2490477
## connectYes 3.813e-02 0.1952748 -0.34
## scale(streng) 6.163e-07 0.0007851 0.62 -0.95
## Residual 6.339e-01 0.7961835
## Number of obs: 589488, groups: traits, 296; subID, 249
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -1.229e-02 3.539e-02 4.278e+02 -0.347 0.7285
## connectYes 1.658e-01 1.339e-02 2.445e+02 12.379 < 2e-16 ***
## scale(streng) -2.572e-03 1.273e-03 3.250e+05 -2.021 0.0433 *
## connectYes:scale(streng) 1.990e-02 4.873e-03 1.350e+05 4.083 4.44e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) cnnctY scl(s)
## connectYes -0.143
## scal(strng) 0.011 -0.035
## cnnctYs:s() 0.000 -0.026 -0.252
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')connect.streng2.plot <- ggpredict(connect.streng2, c("streng","connect")) %>% plot(show.title=FALSE) + jtools::theme_apa() + xlab("Strength of Identification") + ylab("Self-Evaluation")
connect.streng2.plot
Combined
plotCommAxes(connect.streng1.plot, connect.streng2.plot, "Strength of Identification", "Self-Evaluation")
Effect of Identity Typicality Depends on Size
Size Difference
connect.size2 <- lmer(scale(selfResp) ~ connect * scale(sizeD) + ( connect + scale(sizeD) | subID ) + ( 1 | traits), data=fullLong2)## boundary (singular) fit: see help('isSingular')summary(connect.size2)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(selfResp) ~ connect * scale(sizeD) + (connect + scale(sizeD) |
## subID) + (1 | traits)
## Data: fullLong2
##
## REML criterion at convergence: 1366642
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.8551 -0.6674 -0.0138 0.6668 4.1965
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## traits (Intercept) 3.002e-01 0.54787
## subID (Intercept) 6.220e-02 0.24941
## connectYes 3.937e-02 0.19841 -0.34
## scale(sizeD) 1.083e-06 0.00104 0.10 -0.97
## Residual 6.325e-01 0.79532
## Number of obs: 572620, groups: traits, 296; subID, 247
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -1.358e-02 3.560e-02 4.276e+02 -0.381 0.703054
## connectYes 1.672e-01 1.367e-02 2.436e+02 12.230 < 2e-16 ***
## scale(sizeD) -2.311e-03 1.227e-03 3.716e+04 -1.883 0.059720 .
## connectYes:scale(sizeD) 1.435e-02 4.187e-03 1.938e+05 3.426 0.000612 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) cnnctY scl(D)
## connectYes -0.141
## scale(sizD) 0.003 -0.050
## cnnctYs:(D) 0.000 -0.022 -0.289
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')connect.size2.plot <- ggpredict(connect.size2, c("sizeD","connect")) %>% plot(show.title=FALSE) + jtools::theme_apa() + xlab("Size Differences") + ylab("Self-Evaluation")
connect.size2.plot
Identity Connections
Traits that are nominated as typical of more identities are evaluated more self-descriptively
Study 1
moconn1 <- lmer(scale(selfResp) ~ scale(IdIn) + ( scale(IdIn) | subID ) + ( 1 | traits), data=traitsPerS1)## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
## Model failed to converge with max|grad| = 0.0195226 (tol = 0.002, component 1)summary(moconn1)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(selfResp) ~ scale(IdIn) + (scale(IdIn) | subID) + (1 |
## traits)
## Data: traitsPerS1
##
## REML criterion at convergence: 174980.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -5.0715 -0.6794 -0.0191 0.6616 3.8040
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## traits (Intercept) 0.26352 0.5133
## subID (Intercept) 0.04077 0.2019
## scale(IdIn) 0.02463 0.1569 -0.12
## Residual 0.62526 0.7907
## Number of obs: 72786, groups: traits, 296; subID, 246
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 0.01208 0.03268 397.14217 0.37 0.712
## scale(IdIn) 0.16488 0.01122 248.97431 14.69 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## scale(IdIn) -0.028
## optimizer (nloptwrap) convergence code: 0 (OK)
## Model failed to converge with max|grad| = 0.0195226 (tol = 0.002, component 1)moconn1.plot <- ggpredict(moconn1, c("IdIn")) %>% plot(show.title=FALSE) + jtools::theme_apa() + xlab("Identity-Associations") + ylab("Self-Evaluation")
moconn1.plot
Study 2
moconn2 <- lmer(scale(selfResp) ~ scale(IdIn) + ( scale(IdIn) | subID ) + ( 1 | traits), data=traitsPerS1)## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
## Model failed to converge with max|grad| = 0.0195226 (tol = 0.002, component 1)summary(moconn2)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(selfResp) ~ scale(IdIn) + (scale(IdIn) | subID) + (1 |
## traits)
## Data: traitsPerS1
##
## REML criterion at convergence: 174980.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -5.0715 -0.6794 -0.0191 0.6616 3.8040
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## traits (Intercept) 0.26352 0.5133
## subID (Intercept) 0.04077 0.2019
## scale(IdIn) 0.02463 0.1569 -0.12
## Residual 0.62526 0.7907
## Number of obs: 72786, groups: traits, 296; subID, 246
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 0.01208 0.03268 397.14217 0.37 0.712
## scale(IdIn) 0.16488 0.01122 248.97431 14.69 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## scale(IdIn) -0.028
## optimizer (nloptwrap) convergence code: 0 (OK)
## Model failed to converge with max|grad| = 0.0195226 (tol = 0.002, component 1)moconn2.plot <- ggpredict(moconn2, c("IdIn")) %>% plot(show.title=FALSE) + jtools::theme_apa() + xlab("Identity-Associations") + ylab("Self-Evaluation")
moconn2.plot
Combined
plotCommAxes(moconn1.plot, moconn2.plot, "Identity-Typicality", "Self-Evaluation")
Identity Overlap
Study 1
sm1<-lmer(scale(selfResp) ~ scale(T.Sim) * scale(streng) + scale(subTend) + scale(traitTend) + ( scale(T.Sim) | subID ) + ( 1 | traits), data=orderDf1, control=lmerControl(optimizer="bobyqa",
optCtrl=list(maxfun=2e5)))
summary(sm1)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(selfResp) ~ scale(T.Sim) * scale(streng) + scale(subTend) +
## scale(traitTend) + (scale(T.Sim) | subID) + (1 | traits)
## Data: orderDf1
## Control: lmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 2e+05))
##
## REML criterion at convergence: 1385983
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -5.4786 -0.6759 -0.0208 0.6605 4.0231
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## traits (Intercept) 0.13072 0.3616
## subID (Intercept) 0.04123 0.2030
## scale(T.Sim) 0.02107 0.1452 -0.21
## Residual 0.62841 0.7927
## Number of obs: 582288, groups: traits, 296; subID, 246
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -5.056e-03 2.471e-02 4.758e+02 -0.205 0.838
## scale(T.Sim) 5.871e-02 9.387e-03 2.481e+02 6.254 1.74e-09
## scale(streng) -5.543e-03 1.240e-03 5.738e+05 -4.469 7.86e-06
## scale(subTend) 1.069e-02 1.272e-02 2.439e+02 0.840 0.402
## scale(traitTend) 3.874e-01 2.104e-02 2.941e+02 18.409 < 2e-16
## scale(T.Sim):scale(streng) 6.828e-03 1.369e-03 5.422e+05 4.987 6.14e-07
##
## (Intercept)
## scale(T.Sim) ***
## scale(streng) ***
## scale(subTend)
## scale(traitTend) ***
## scale(T.Sim):scale(streng) ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) sc(T.S) scl(s) scl(sT) scl(tT)
## scale(T.Sm) -0.109
## scal(strng) -0.001 -0.016
## scal(sbTnd) -0.001 -0.009 0.007
## scl(trtTnd) 0.000 -0.002 0.002 0.001
## scl(T.S):() -0.003 -0.013 0.149 0.001 0.000sm1.plot <- ggpredict(sm1, c("T.Sim", "streng")) %>% plot(show.title=FALSE) + jtools::theme_apa() + xlab("Overlap with Identity") + ylab("Self-Evaluation")
sm1.plot
Study 2
sm2<-lmer(scale(selfResp) ~ scale(T.Sim) * scale(streng) + scale(subTend) + scale(traitTend) + ( scale(T.Sim) | subID ) + ( 1 | traits), data=orderDf2, control=lmerControl(optimizer="bobyqa",
optCtrl=list(maxfun=2e5)))
summary(sm2)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(selfResp) ~ scale(T.Sim) * scale(streng) + scale(subTend) +
## scale(traitTend) + (scale(T.Sim) | subID) + (1 | traits)
## Data: orderDf2
## Control: lmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 2e+05))
##
## REML criterion at convergence: 1388244
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.8282 -0.6618 -0.0138 0.6587 4.2663
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## traits (Intercept) 0.14988 0.3871
## subID (Intercept) 0.06194 0.2489
## scale(T.Sim) 0.01701 0.1304 -0.22
## Residual 0.62445 0.7902
## Number of obs: 584752, groups: traits, 296; subID, 247
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -5.726e-04 2.754e-02 5.069e+02 -0.021 0.98342
## scale(T.Sim) 4.703e-02 8.433e-03 2.485e+02 5.577 6.37e-08
## scale(streng) -3.947e-03 1.250e-03 5.802e+05 -3.158 0.00159
## scale(subTend) 1.362e-02 1.553e-02 2.454e+02 0.877 0.38146
## scale(traitTend) 3.621e-01 2.253e-02 2.941e+02 16.075 < 2e-16
## scale(T.Sim):scale(streng) -1.321e-03 1.349e-03 5.184e+05 -0.979 0.32754
##
## (Intercept)
## scale(T.Sim) ***
## scale(streng) **
## scale(subTend)
## scale(traitTend) ***
## scale(T.Sim):scale(streng)
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) sc(T.S) scl(s) scl(sT) scl(tT)
## scale(T.Sm) -0.120
## scal(strng) 0.000 -0.010
## scal(sbTnd) -0.001 -0.008 0.002
## scl(trtTnd) 0.000 -0.002 0.001 0.000
## scl(T.S):() -0.002 -0.009 0.105 0.000 0.000sm2.plot <- ggpredict(sm2, c("T.Sim", "streng")) %>% plot(show.title=FALSE) + jtools::theme_apa() + xlab("Overlap with Identity") + ylab("Self-Evaluation")
sm2.plot
Combined Plot
plotCommAxes(sm1.plot, sm2.plot, "Identity Overlap", "Self-Evaluation")
Identity Distance
Study 1
dm1<-lmer(scale(selfResp) ~ scale(order) * scale(streng) + scale(subTend) + scale(traitTend) + ( scale(order) | subID ) + ( 1 | traits), data=orderDf1)## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
## Model failed to converge with max|grad| = 0.00884777 (tol = 0.002, component 1)summary(dm1)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(selfResp) ~ scale(order) * scale(streng) + scale(subTend) +
## scale(traitTend) + (scale(order) | subID) + (1 | traits)
## Data: orderDf1
##
## REML criterion at convergence: 1355429
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.3472 -0.6756 -0.0174 0.6693 4.1093
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## traits (Intercept) 0.13692 0.3700
## subID (Intercept) 0.03994 0.1999
## scale(order) 0.01384 0.1176 0.18
## Residual 0.63517 0.7970
## Number of obs: 566898, groups: traits, 296; subID, 246
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -4.469e-03 2.503e-02 4.666e+02 -0.179 0.85834
## scale(order) -6.429e-02 7.620e-03 2.474e+02 -8.438 2.74e-15
## scale(streng) -4.114e-03 1.258e-03 5.599e+05 -3.272 0.00107
## scale(subTend) 1.298e-02 1.260e-02 2.445e+02 1.031 0.30373
## scale(traitTend) 3.937e-01 2.154e-02 2.944e+02 18.281 < 2e-16
## scale(order):scale(streng) -9.300e-03 1.281e-03 4.868e+05 -7.261 3.85e-13
##
## (Intercept)
## scale(order) ***
## scale(streng) **
## scale(subTend)
## scale(traitTend) ***
## scale(order):scale(streng) ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) scl(r) scl(s) scl(sT) scl(tT)
## scale(ordr) 0.091
## scal(strng) 0.000 0.014
## scal(sbTnd) 0.000 0.005 0.007
## scl(trtTnd) 0.000 0.002 0.001 0.000
## scl(rdr):() 0.002 0.005 -0.053 0.001 0.001
## optimizer (nloptwrap) convergence code: 0 (OK)
## Model failed to converge with max|grad| = 0.00884777 (tol = 0.002, component 1)dm1.plot <- ggpredict(dm1, c("order", "streng")) %>% plot(show.title=FALSE) + jtools::theme_apa() + xlab("Distance from Identity") + ylab("Self-Evaluation")
dm1.plot
Study 2
dm2<-lmer(scale(selfResp) ~ scale(order) * scale(streng) + scale(subTend) + scale(traitTend) + ( scale(order) | subID ) + ( 1 | traits), data=orderDf2)## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
## Model failed to converge with max|grad| = 0.0084477 (tol = 0.002, component 1)summary(dm2)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(selfResp) ~ scale(order) * scale(streng) + scale(subTend) +
## scale(traitTend) + (scale(order) | subID) + (1 | traits)
## Data: orderDf2
##
## REML criterion at convergence: 1374845
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.0795 -0.6635 -0.0129 0.6634 4.2017
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## traits (Intercept) 0.154257 0.39276
## subID (Intercept) 0.061307 0.24760
## scale(order) 0.009124 0.09552 0.22
## Residual 0.632151 0.79508
## Number of obs: 576170, groups: traits, 296; subID, 247
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -2.815e-03 2.776e-02 5.023e+02 -0.101 0.919263
## scale(order) -5.488e-02 6.222e-03 2.491e+02 -8.820 < 2e-16
## scale(streng) -2.985e-03 1.263e-03 5.720e+05 -2.364 0.018082
## scale(subTend) 1.459e-02 1.543e-02 2.445e+02 0.945 0.345468
## scale(traitTend) 3.623e-01 2.285e-02 2.940e+02 15.852 < 2e-16
## scale(order):scale(streng) -4.490e-03 1.218e-03 4.239e+05 -3.685 0.000229
##
## (Intercept)
## scale(order) ***
## scale(streng) *
## scale(subTend)
## scale(traitTend) ***
## scale(order):scale(streng) ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) scl(r) scl(s) scl(sT) scl(tT)
## scale(ordr) 0.121
## scal(strng) 0.000 0.008
## scal(sbTnd) 0.000 0.005 0.001
## scl(trtTnd) 0.000 0.002 0.000 0.000
## scl(rdr):() 0.000 0.001 -0.051 0.002 0.001
## optimizer (nloptwrap) convergence code: 0 (OK)
## Model failed to converge with max|grad| = 0.0084477 (tol = 0.002, component 1)dm2.plot <- ggpredict(dm2, c("order", "streng")) %>% plot(show.title=FALSE) + jtools::theme_apa() + xlab("Distance from Identity") + ylab("Self-Evaluation")
dm2.plot
Combined Plot
plotCommAxes(dm1.plot, dm2.plot, "Identity Distance", "Self-Evaluation")
Identity Similarity and Distance Joint Influence on Self-Evaluations (Principal Component)
Study 1
pca1<-lmer(scale(selfResp) ~ scale(PCAdist) * scale(streng) + scale(subTend) + scale(traitTend) + ( scale(PCAdist) | subID ) + ( 1 | traits), data=orderDf1)
summary(pca1)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(selfResp) ~ scale(PCAdist) * scale(streng) + scale(subTend) +
## scale(traitTend) + (scale(PCAdist) | subID) + (1 | traits)
## Data: orderDf1
##
## REML criterion at convergence: 1348143
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.6482 -0.6714 -0.0188 0.6621 4.2617
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## traits (Intercept) 0.13138 0.3625
## subID (Intercept) 0.04217 0.2054
## scale(PCAdist) 0.02132 0.1460 -0.24
## Residual 0.62694 0.7918
## Number of obs: 566898, groups: traits, 296; subID, 246
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -6.219e-03 2.483e-02 4.778e+02 -0.250 0.802
## scale(PCAdist) 7.259e-02 9.418e-03 2.480e+02 7.708 3.08e-13
## scale(streng) -6.255e-03 1.259e-03 5.596e+05 -4.969 6.74e-07
## scale(subTend) 6.148e-03 1.277e-02 2.444e+02 0.481 0.631
## scale(traitTend) 3.839e-01 2.110e-02 2.942e+02 18.196 < 2e-16
## scale(PCAdist):scale(streng) 9.040e-03 1.314e-03 5.294e+05 6.878 6.06e-12
##
## (Intercept)
## scale(PCAdist) ***
## scale(streng) ***
## scale(subTend)
## scale(traitTend) ***
## scale(PCAdist):scale(streng) ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) sc(PCA) scl(s) scl(sT) scl(tT)
## scl(PCAdst) -0.126
## scal(strng) 0.000 -0.016
## scal(sbTnd) 0.000 -0.007 0.008
## scl(trtTnd) 0.000 -0.003 0.002 0.001
## scl(PCA):() -0.003 -0.003 0.103 -0.001 0.000pca1.plot <- ggpredict(pca1, c("PCAdist", "streng")) %>% plot(show.title=FALSE) + jtools::theme_apa() + xlab("Composite Identity-Overlap") + ylab("Self-Evaluation")
pca1.plot
Study 2
pca2<-lmer(scale(selfResp) ~ scale(PCAdist) * scale(streng) + scale(subTend) + scale(traitTend) + ( scale(PCAdist) | subID ) + ( 1 | traits), data=orderDf2)## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
## Model failed to converge with max|grad| = 0.0132975 (tol = 0.002, component 1)summary(pca2)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(selfResp) ~ scale(PCAdist) * scale(streng) + scale(subTend) +
## scale(traitTend) + (scale(PCAdist) | subID) + (1 | traits)
## Data: orderDf2
##
## REML criterion at convergence: 1369642
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.6597 -0.6602 -0.0132 0.6597 4.2286
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## traits (Intercept) 0.14953 0.3867
## subID (Intercept) 0.06341 0.2518
## scale(PCAdist) 0.01572 0.1254 0.25
## Residual 0.62633 0.7914
## Number of obs: 576170, groups: traits, 296; subID, 247
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -2.851e-03 2.763e-02 5.093e+02 -0.103 0.917860
## scale(PCAdist) -6.009e-02 8.097e-03 2.491e+02 -7.422 1.83e-12
## scale(streng) -4.546e-03 1.263e-03 5.719e+05 -3.598 0.000321
## scale(subTend) 1.001e-02 1.557e-02 2.455e+02 0.643 0.520644
## scale(traitTend) 3.568e-01 2.250e-02 2.939e+02 15.854 < 2e-16
## scale(PCAdist):scale(streng) -2.598e-03 1.277e-03 5.077e+05 -2.034 0.041923
##
## (Intercept)
## scale(PCAdist) ***
## scale(streng) ***
## scale(subTend)
## scale(traitTend) ***
## scale(PCAdist):scale(streng) *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) sc(PCA) scl(s) scl(sT) scl(tT)
## scl(PCAdst) 0.144
## scal(strng) 0.000 0.009
## scal(sbTnd) 0.000 0.006 0.002
## scl(trtTnd) 0.000 0.002 0.001 0.000
## scl(PCA):() 0.001 -0.003 -0.079 0.002 0.001
## optimizer (nloptwrap) convergence code: 0 (OK)
## Model failed to converge with max|grad| = 0.0132975 (tol = 0.002, component 1)pca2.plot <- ggpredict(pca2, c("PCAdist", "streng")) %>% plot(show.title=FALSE) + jtools::theme_apa() + xlab("Composite Identity-Overlap") + ylab("Self-Evaluation")
pca2.plot
Combined Plot
plotCommAxes(pca1.plot, pca2.plot, "Composite Identity-Overlap", "Self-Evaluation")
Identity Centrality Predicts Strength of Identification
Study 1
I2I1.streng<-lmer(scale(streng) ~ scale(I2Ideg) + ( scale(I2Ideg) | subID ) + ( 1 | id), data=idShort1)## boundary (singular) fit: see help('isSingular')summary(I2I1.streng)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(streng) ~ scale(I2Ideg) + (scale(I2Ideg) | subID) + (1 |
## id)
## Data: idShort1
##
## REML criterion at convergence: 5135.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -5.7156 -0.3367 0.1702 0.6259 2.1894
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subID (Intercept) 0.1817128 0.42628
## scale(I2Ideg) 0.0007822 0.02797 1.00
## id (Intercept) 0.1633094 0.40412
## Residual 0.6767897 0.82267
## Number of obs: 1968, groups: subID, 246; id, 8
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 2.663e-03 1.466e-01 7.507e+00 0.018 0.9860
## scale(I2Ideg) 6.071e-02 2.810e-02 3.183e+02 2.161 0.0315 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## scal(I2Idg) 0.018
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')I2I1.streng.plot <- ggpredict(I2I1.streng, c("I2Ideg")) %>% plot(show.title=FALSE) + jtools::theme_apa() + xlab("Identity-to-Identity Centrality") + ylab("Self-Evaluation")
I2I1.streng.plot
Study 2
I2I2.streng<-lmer(scale(streng) ~ scale(I2Ideg) + ( scale(I2Ideg) | subID ) + ( 1 | id), data=idShort2)
summary(I2I2.streng)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(streng) ~ scale(I2Ideg) + (scale(I2Ideg) | subID) + (1 |
## id)
## Data: idShort2
##
## REML criterion at convergence: 5022.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -5.6212 -0.3770 0.1274 0.6080 2.6012
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subID (Intercept) 0.21196 0.4604
## scale(I2Ideg) 0.01029 0.1014 0.24
## id (Intercept) 0.19078 0.4368
## Residual 0.61342 0.7832
## Number of obs: 1976, groups: subID, 247; id, 8
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 4.786e-03 1.582e-01 7.518e+00 0.030 0.97666
## scale(I2Ideg) 7.761e-02 2.796e-02 1.494e+02 2.775 0.00622 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## scal(I2Idg) 0.020I2I2.streng.plot <- ggpredict(I2I2.streng, c("I2Ideg")) %>% plot(show.title=FALSE) + jtools::theme_apa() + xlab("Identity-to-Identity Centrality") + ylab("Self-Evaluation")
I2I2.streng.plot
Combined Plot
plotCommAxes(I2I1.streng.plot, I2I2.streng.plot, "Identity-to-Identity Centrality", "Strength of Identification")
Strength of Identification Predicts More Shared Traits Between Identities
Study 1
TSharedStreng1 <- lmer( scale(traitCommNod) ~ scale(streng) + ( scale(streng) | subID) + ( 1 | id), data=idShort1)
summary(TSharedStreng1)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(traitCommNod) ~ scale(streng) + (scale(streng) | subID) +
## (1 | id)
## Data: idShort1
##
## REML criterion at convergence: 3387.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -5.8325 -0.5313 0.0625 0.6032 4.0899
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subID (Intercept) 0.778870 0.88254
## scale(streng) 0.018863 0.13734 0.51
## id (Intercept) 0.009726 0.09862
## Residual 0.201142 0.44849
## Number of obs: 1968, groups: subID, 246; id, 8
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -0.002885 0.067058 68.777738 -0.043 0.966
## scale(streng) 0.065984 0.016600 204.721147 3.975 9.76e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## scal(strng) 0.215TSharedStreng1.plot <- ggpredict(TSharedStreng1, c("streng")) %>% plot(show.title=FALSE) + jtools::theme_apa() + xlab("Strength of Identification") + ylab("Shared Traits")Study 2
TSharedStreng2 <- lmer( scale(traitCommNod) ~ scale(streng) + ( scale(streng) | subID) + ( 1 | id), data=idShort2)
summary(TSharedStreng2)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(traitCommNod) ~ scale(streng) + (scale(streng) | subID) +
## (1 | id)
## Data: idShort2
##
## REML criterion at convergence: 3404.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.5612 -0.5208 0.0559 0.6180 3.5174
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subID (Intercept) 0.790216 0.88894
## scale(streng) 0.005263 0.07255 0.60
## id (Intercept) 0.006149 0.07842
## Residual 0.208387 0.45649
## Number of obs: 1976, groups: subID, 247; id, 8
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 0.00116 0.06385 107.71861 0.018 0.985539
## scale(streng) 0.05655 0.01465 181.90740 3.860 0.000157 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## scal(strng) 0.165TSharedStreng2.plot <- ggpredict(TSharedStreng2, c("streng")) %>% plot(show.title=FALSE) + jtools::theme_apa() + xlab("Strength of Identification") + ylab("Shared Traits")Combined Plot
ggpubr::ggarrange(TSharedStreng1.plot, TSharedStreng2.plot)
plotCommAxes(TSharedStreng1.plot, TSharedStreng2.plot, "Strength of Identification", "Proportion of Traits in Common")
Does positvity of identity predict asymmetries in valenced content?
Study 1
asym.pos1 <-lmer(pos ~ pndiff + ( pndiff | subID) + (1 | id), data=idShort1)## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
## Model failed to converge with max|grad| = 0.0303214 (tol = 0.002, component 1)summary(asym.pos1)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: pos ~ pndiff + (pndiff | subID) + (1 | id)
## Data: idShort1
##
## REML criterion at convergence: 6431
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.5569 -0.5576 0.1762 0.6505 2.7125
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subID (Intercept) 0.4498030 0.67067
## pndiff 0.0001272 0.01128 -0.73
## id (Intercept) 0.3195272 0.56527
## Residual 1.2806356 1.13165
## Number of obs: 1968, groups: subID, 246; id, 8
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 5.609290 0.206725 7.801213 27.134 5.28e-09 ***
## pndiff 0.014967 0.001928 66.044743 7.765 6.96e-11 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## pndiff -0.131
## optimizer (nloptwrap) convergence code: 0 (OK)
## Model failed to converge with max|grad| = 0.0303214 (tol = 0.002, component 1)Study 2
asym.pos2 <-lmer(pos ~ pndiff + ( pndiff | subID) + (1 | id), data=idShort2)## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
## Model failed to converge with max|grad| = 0.651478 (tol = 0.002, component 1)summary(asym.pos2)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: pos ~ pndiff + (pndiff | subID) + (1 | id)
## Data: idShort2
##
## REML criterion at convergence: 6368.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.3048 -0.5398 0.1799 0.6726 2.1612
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subID (Intercept) 0.3769815 0.61399
## pndiff 0.0001139 0.01067 -0.68
## id (Intercept) 0.3259019 0.57088
## Residual 1.2396391 1.11339
## Number of obs: 1976, groups: subID, 247; id, 8
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 5.700323 0.207770 5.291264 27.436 6.55e-07 ***
## pndiff 0.013335 0.002208 28.543767 6.039 1.52e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## pndiff -0.112
## optimizer (nloptwrap) convergence code: 0 (OK)
## Model failed to converge with max|grad| = 0.651478 (tol = 0.002, component 1)More trait overlap predicts stronger group identification
Study 1
tcomm.streng1 <-lmer(scale(streng) ~ scale(traitCommNod) + ( scale(traitCommNod) | subID) + (1 | id), data=idShort1)
summary(tcomm.streng1)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(streng) ~ scale(traitCommNod) + (scale(traitCommNod) |
## subID) + (1 | id)
## Data: idShort1
##
## REML criterion at convergence: 5129.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -5.5840 -0.3318 0.1614 0.6371 2.3258
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subID (Intercept) 0.17637 0.4200
## scale(traitCommNod) 0.01621 0.1273 -0.10
## id (Intercept) 0.15736 0.3967
## Residual 0.66902 0.8179
## Number of obs: 1968, groups: subID, 246; id, 8
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -0.0004936 0.1441315 7.5407501 -0.003 0.99736
## scale(traitCommNod) 0.0919415 0.0296902 53.5611864 3.097 0.00311 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## scl(trtCmN) 0.001Study 2
tcomm.streng2 <-lmer(scale(streng) ~ scale(traitCommNod) + ( scale(traitCommNod) | subID) + (1 | id), data=idShort2)## boundary (singular) fit: see help('isSingular')summary(tcomm.streng2)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(streng) ~ scale(traitCommNod) + (scale(traitCommNod) |
## subID) + (1 | id)
## Data: idShort2
##
## REML criterion at convergence: 5026.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -5.5692 -0.3732 0.1179 0.6107 2.6993
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subID (Intercept) 0.2213026 0.47043
## scale(traitCommNod) 0.0002155 0.01468 -1.00
## id (Intercept) 0.1876325 0.43317
## Residual 0.6170142 0.78550
## Number of obs: 1976, groups: subID, 247; id, 8
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -0.001446 0.157044 7.537281 -0.009 0.9929
## scale(traitCommNod) 0.064150 0.027591 326.148512 2.325 0.0207 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## scl(trtCmN) -0.010
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')More identity overlap predicts stronger group identification
Study 1
icomm.streng1 <-lmer(scale(streng) ~ scale(idCommNod) + ( scale(idCommNod) | subID) + (1 | id), data=idShort1)
summary(icomm.streng1)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(streng) ~ scale(idCommNod) + (scale(idCommNod) | subID) +
## (1 | id)
## Data: idShort1
##
## REML criterion at convergence: 5134.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -5.6705 -0.3384 0.1697 0.6257 2.2069
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subID (Intercept) 0.16657 0.4081
## scale(idCommNod) 0.02676 0.1636 0.23
## id (Intercept) 0.16302 0.4038
## Residual 0.67173 0.8196
## Number of obs: 1968, groups: subID, 246; id, 8
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 0.00205 0.14655 7.51862 0.014 0.9892
## scale(idCommNod) 0.05597 0.03220 98.60471 1.738 0.0853 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## scl(dCmmNd) 0.032Study 2
icomm.streng2 <-lmer(scale(streng) ~ scale(idCommNod) + ( scale(idCommNod) | subID) + (1 | id), data=idShort2)
summary(icomm.streng2)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(streng) ~ scale(idCommNod) + (scale(idCommNod) | subID) +
## (1 | id)
## Data: idShort2
##
## REML criterion at convergence: 5025.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -5.6236 -0.3644 0.1267 0.6091 2.2873
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subID (Intercept) 0.19083 0.4368
## scale(idCommNod) 0.02119 0.1456 0.39
## id (Intercept) 0.18922 0.4350
## Residual 0.61844 0.7864
## Number of obs: 1976, groups: subID, 247; id, 8
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 0.002475 0.157462 7.491891 0.016 0.988
## scale(idCommNod) -0.023603 0.030195 157.227342 -0.782 0.436
##
## Correlation of Fixed Effects:
## (Intr)
## scl(dCmmNd) 0.038Individual Differences Predict Similarity in Identity Judgments
m <-lmer(scale(posDist) ~ scale(SE) + ( scale(SE) | subID), data=idSim1)## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
## Model failed to converge with max|grad| = 0.0296702 (tol = 0.002, component 1)summary(m)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(posDist) ~ scale(SE) + (scale(SE) | subID)
## Data: idSim1
##
## REML criterion at convergence: 18732.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.2789 -0.7439 -0.1736 0.6267 3.7725
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subID (Intercept) 0.1468940 0.38327
## scale(SE) 0.0002704 0.01644 0.82
## Residual 0.8421904 0.91771
## Number of obs: 6860, groups: subID, 245
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -0.00247 0.02690 242.21879 -0.092 0.9269
## scale(SE) -0.09315 0.02695 160.08071 -3.457 0.0007 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## scale(SE) 0.059
## optimizer (nloptwrap) convergence code: 0 (OK)
## Model failed to converge with max|grad| = 0.0296702 (tol = 0.002, component 1)m <-lmer(scale(strengDist) ~ scale(NFC) + ( scale(NFC) | subID), data=idSim1)
summary(m)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(strengDist) ~ scale(NFC) + (scale(NFC) | subID)
## Data: idSim1
##
## REML criterion at convergence: 18474
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.6773 -0.6982 -0.1291 0.6265 4.4272
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subID (Intercept) 0.16739 0.4091
## scale(NFC) 0.03351 0.1831 0.05
## Residual 0.79366 0.8909
## Number of obs: 6888, groups: subID, 246
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 0.006492 0.030100 239.301125 0.216 0.82943
## scale(NFC) -0.100677 0.033420 117.159814 -3.012 0.00318 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## scale(NFC) 0.007m <-lmer(scale(posDist) ~ scale(DS) + ( 1 | subID), data=idSim1)
summary(m)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(posDist) ~ scale(DS) + (1 | subID)
## Data: idSim1
##
## REML criterion at convergence: 18835.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.3055 -0.7358 -0.1728 0.6372 3.7564
##
## Random effects:
## Groups Name Variance Std.Dev.
## subID (Intercept) 0.150 0.3873
## Residual 0.845 0.9192
## Number of obs: 6888, groups: subID, 246
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -8.091e-15 2.706e-02 2.440e+02 0.000 1.00000
## scale(DS) 7.973e-02 2.707e-02 2.440e+02 2.946 0.00353 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## scale(DS) 0.000m <-lmer(scale(posDist) ~ scale(SCC) + ( 1 | subID), data=idSim1)
summary(m)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(posDist) ~ scale(SCC) + (1 | subID)
## Data: idSim1
##
## REML criterion at convergence: 18839.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.2954 -0.7458 -0.1698 0.6326 3.7620
##
## Random effects:
## Groups Name Variance Std.Dev.
## subID (Intercept) 0.1532 0.3914
## Residual 0.8450 0.9192
## Number of obs: 6888, groups: subID, 246
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -8.551e-15 2.730e-02 2.440e+02 0.000 1.0000
## scale(SCC) -5.634e-02 2.731e-02 2.440e+02 -2.063 0.0401 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## scale(SCC) 0.000m <-lmer(scale(posDist) ~ scale(MemSE) + ( 1 | subID), data=idSim1)
summary(m)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(posDist) ~ scale(MemSE) + (1 | subID)
## Data: idSim1
##
## REML criterion at convergence: 18836.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.2769 -0.7369 -0.1764 0.6291 3.7572
##
## Random effects:
## Groups Name Variance Std.Dev.
## subID (Intercept) 0.1508 0.3884
## Residual 0.8450 0.9192
## Number of obs: 6888, groups: subID, 246
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -8.325e-15 2.713e-02 2.440e+02 0.000 1.00000
## scale(MemSE) -7.438e-02 2.713e-02 2.440e+02 -2.742 0.00656 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## scale(MmSE) 0.000m <-lmer(scale(posDist) ~ scale(PrivCSE) + ( 1 | subID), data=idSim1)
summary(m)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(posDist) ~ scale(PrivCSE) + (1 | subID)
## Data: idSim1
##
## REML criterion at convergence: 18840.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.2879 -0.7390 -0.1779 0.6307 3.7496
##
## Random effects:
## Groups Name Variance Std.Dev.
## subID (Intercept) 0.1541 0.3926
## Residual 0.8450 0.9192
## Number of obs: 6888, groups: subID, 246
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -8.349e-15 2.737e-02 2.440e+02 0.00 1.0000
## scale(PrivCSE) -4.790e-02 2.737e-02 2.440e+02 -1.75 0.0814 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## scl(PrvCSE) 0.000m <-lmer(scale(posDist) ~ scale(PubCSE) + ( 1 | subID), data=idSim1)
summary(m)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(posDist) ~ scale(PubCSE) + (1 | subID)
## Data: idSim1
##
## REML criterion at convergence: 18838.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.2988 -0.7332 -0.1769 0.6322 3.7589
##
## Random effects:
## Groups Name Variance Std.Dev.
## subID (Intercept) 0.1525 0.3906
## Residual 0.8450 0.9192
## Number of obs: 6888, groups: subID, 246
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -7.974e-15 2.725e-02 2.440e+02 0.000 1.000
## scale(PubCSE) -6.192e-02 2.726e-02 2.440e+02 -2.272 0.024 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## scal(PbCSE) 0.000m <-lmer(scale(posDist) ~ scale(MemSE) + ( 1 | subID), data=idSim1)
summary(m)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(posDist) ~ scale(MemSE) + (1 | subID)
## Data: idSim1
##
## REML criterion at convergence: 18836.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.2769 -0.7369 -0.1764 0.6291 3.7572
##
## Random effects:
## Groups Name Variance Std.Dev.
## subID (Intercept) 0.1508 0.3884
## Residual 0.8450 0.9192
## Number of obs: 6888, groups: subID, 246
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -8.325e-15 2.713e-02 2.440e+02 0.000 1.00000
## scale(MemSE) -7.438e-02 2.713e-02 2.440e+02 -2.742 0.00656 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## scale(MmSE) 0.000Individual Differences Predict Pairwise Overlap in Traits in Common
Study 1
Positive
m <-lmer(scale(idtpSim) ~ scale(SCC) + scale(idtnSim) + ( scale(idtnSim) | subID), data=idSim1)## boundary (singular) fit: see help('isSingular')summary(m)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(idtpSim) ~ scale(SCC) + scale(idtnSim) + (scale(idtnSim) |
## subID)
## Data: idSim1
##
## REML criterion at convergence: 16716.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.4786 -0.6413 -0.1864 0.5161 7.0170
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subID (Intercept) 0.4005422 0.63288
## scale(idtnSim) 0.0006437 0.02537 1.00
## Residual 0.5947587 0.77121
## Number of obs: 6888, groups: subID, 246
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -4.423e-03 4.141e-02 2.426e+02 -0.107 0.915
## scale(SCC) 4.557e-02 4.092e-02 2.360e+02 1.114 0.267
## scale(idtnSim) 2.107e-02 1.298e-02 3.082e+03 1.623 0.105
##
## Correlation of Fixed Effects:
## (Intr) s(SCC)
## scale(SCC) 0.000
## scal(dtnSm) 0.127 0.003
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')m <-lmer(scale(idtpSim) ~ scale(Ind) + scale(idtnSim) + ( scale(idtnSim) | subID), data=idSim1)## boundary (singular) fit: see help('isSingular')summary(m)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(idtpSim) ~ scale(Ind) + scale(idtnSim) + (scale(idtnSim) |
## subID)
## Data: idSim1
##
## REML criterion at convergence: 16702
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.4908 -0.6446 -0.1879 0.5126 7.0203
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subID (Intercept) 0.3751958 0.61253
## scale(idtnSim) 0.0005848 0.02418 1.00
## Residual 0.5948167 0.77124
## Number of obs: 6888, groups: subID, 246
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -0.00438 0.04015 242.33298 -0.109 0.9132
## scale(Ind) 0.16080 0.03989 240.59501 4.031 7.44e-05 ***
## scale(idtnSim) 0.02187 0.01295 3209.66171 1.689 0.0914 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) scl(I)
## scale(Ind) 0.000
## scal(dtnSm) 0.122 0.003
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')m <-lmer(scale(idtpSim) ~ scale(Inter) + scale(idtnSim) + ( scale(idtnSim) | subID), data=idSim1)## boundary (singular) fit: see help('isSingular')summary(m)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(idtpSim) ~ scale(Inter) + scale(idtnSim) + (scale(idtnSim) |
## subID)
## Data: idSim1
##
## REML criterion at convergence: 16711.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.4824 -0.6431 -0.1881 0.5151 7.0156
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subID (Intercept) 0.3927089 0.62666
## scale(idtnSim) 0.0006496 0.02549 1.00
## Residual 0.5947618 0.77121
## Number of obs: 6888, groups: subID, 246
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -4.357e-03 4.102e-02 2.426e+02 -0.106 0.9155
## scale(Inter) 9.836e-02 4.069e-02 2.402e+02 2.417 0.0164 *
## scale(idtnSim) 2.070e-02 1.298e-02 3.035e+03 1.595 0.1107
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) scl(I)
## scale(Intr) 0.001
## scal(dtnSm) 0.128 -0.017
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')m <-lmer(scale(idtpSim) ~ scale(SWLS) + scale(idtnSim) + ( scale(idtnSim) | subID), data=idSim1)## boundary (singular) fit: see help('isSingular')summary(m)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(idtpSim) ~ scale(SWLS) + scale(idtnSim) + (scale(idtnSim) |
## subID)
## Data: idSim1
##
## REML criterion at convergence: 16711.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.4797 -0.6423 -0.1871 0.5160 7.0191
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subID (Intercept) 0.3916795 0.62584
## scale(idtnSim) 0.0005174 0.02275 1.00
## Residual 0.5948193 0.77125
## Number of obs: 6888, groups: subID, 246
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -3.968e-03 4.097e-02 2.425e+02 -0.097 0.9229
## scale(SWLS) 9.839e-02 4.068e-02 2.400e+02 2.419 0.0163 *
## scale(idtnSim) 2.099e-02 1.295e-02 3.381e+03 1.620 0.1052
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) s(SWLS
## scale(SWLS) 0.000
## scal(dtnSm) 0.114 -0.005
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')m <-lmer(scale(idtpSim) ~ scale(IdImp) + scale(idtnSim) + ( scale(idtnSim) | subID), data=idSim1)## boundary (singular) fit: see help('isSingular')summary(m)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(idtpSim) ~ scale(IdImp) + scale(idtnSim) + (scale(idtnSim) |
## subID)
## Data: idSim1
##
## REML criterion at convergence: 16717.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.4736 -0.6428 -0.1864 0.5163 7.0170
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subID (Intercept) 0.4017482 0.6338
## scale(idtnSim) 0.0005903 0.0243 1.00
## Residual 0.5947830 0.7712
## Number of obs: 6888, groups: subID, 246
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -4.186e-03 4.147e-02 2.426e+02 -0.101 0.920
## scale(IdImp) 2.163e-02 4.120e-02 2.416e+02 0.525 0.600
## scale(idtnSim) 2.079e-02 1.297e-02 3.183e+03 1.603 0.109
##
## Correlation of Fixed Effects:
## (Intr) sc(II)
## scal(IdImp) 0.002
## scal(dtnSm) 0.122 -0.021
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')m <-lmer(scale(idtpSim) ~ scale(phi) + scale(idtnSim) + ( scale(idtnSim) | subID), data=idSim1)## boundary (singular) fit: see help('isSingular')summary(m)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(idtpSim) ~ scale(phi) + scale(idtnSim) + (scale(idtnSim) |
## subID)
## Data: idSim1
##
## REML criterion at convergence: 16710.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.4715 -0.6405 -0.1925 0.5159 7.0232
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subID (Intercept) 0.3906973 0.6251
## scale(idtnSim) 0.0006454 0.0254 1.00
## Residual 0.5947354 0.7712
## Number of obs: 6888, groups: subID, 246
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -4.231e-03 4.092e-02 2.427e+02 -0.103 0.91775
## scale(phi) -1.100e-01 4.055e-02 2.399e+02 -2.712 0.00716 **
## scale(idtnSim) 2.071e-02 1.297e-02 3.076e+03 1.596 0.11060
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) scl(p)
## scale(phi) -0.002
## scal(dtnSm) 0.127 0.017
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')m <-lmer(scale(idtpSim) ~ scale(overlap_norm) + scale(idtnSim) + ( scale(idtnSim) | subID), data=idSim1)## boundary (singular) fit: see help('isSingular')summary(m)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula:
## scale(idtpSim) ~ scale(overlap_norm) + scale(idtnSim) + (scale(idtnSim) |
## subID)
## Data: idSim1
##
## REML criterion at convergence: 16239.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.6903 -0.6401 -0.2083 0.5215 7.0918
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subID (Intercept) 3.778e-02 0.194382
## scale(idtnSim) 3.785e-06 0.001946 1.00
## Residual 5.950e-01 0.771349
## Number of obs: 6888, groups: subID, 246
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -6.631e-05 1.549e-02 2.416e+02 -0.004 0.997
## scale(overlap_norm) 6.028e-01 1.563e-02 2.477e+02 38.560 <2e-16 ***
## scale(idtnSim) 1.526e-02 1.140e-02 2.282e+03 1.339 0.181
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) scl(_)
## scl(vrlp_n) 0.001
## scal(dtnSm) 0.011 -0.134
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')m <-lmer(scale(idtpSim) ~ scale(H_index) + scale(idtnSim) + ( scale(idtnSim) | subID), data=idSim1)## boundary (singular) fit: see help('isSingular')summary(m)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(idtpSim) ~ scale(H_index) + scale(idtnSim) + (scale(idtnSim) |
## subID)
## Data: idSim1
##
## REML criterion at convergence: 16487.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.2396 -0.6200 -0.1983 0.5115 7.0483
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subID (Intercept) 0.1443075 0.37988
## scale(idtnSim) 0.0003823 0.01955 1.00
## Residual 0.5946927 0.77116
## Number of obs: 6888, groups: subID, 246
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -2.704e-04 2.595e-02 2.435e+02 -0.010 0.992
## scale(H_index) 5.087e-01 2.611e-02 2.524e+02 19.484 <2e-16 ***
## scale(idtnSim) 1.038e-02 1.258e-02 2.877e+03 0.825 0.410
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) sc(H_)
## scal(H_ndx) 0.009
## scal(dtnSm) 0.102 -0.109
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')m <-lmer(scale(idtpSim) ~ scale(SE) + scale(idtnSim) + ( scale(idtnSim)| subID), data=idSim1)## boundary (singular) fit: see help('isSingular')summary(m)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(idtpSim) ~ scale(SE) + scale(idtnSim) + (scale(idtnSim) |
## subID)
## Data: idSim1
##
## REML criterion at convergence: 16640.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.4824 -0.6459 -0.1823 0.5173 7.0192
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subID (Intercept) 0.3947209 0.62827
## scale(idtnSim) 0.0005584 0.02363 1.00
## Residual 0.5943377 0.77093
## Number of obs: 6860, groups: subID, 245
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -1.734e-03 4.120e-02 2.415e+02 -0.042 0.9665
## scale(SE) 8.615e-02 4.073e-02 2.340e+02 2.115 0.0355 *
## scale(idtnSim) 2.233e-02 1.299e-02 3.250e+03 1.719 0.0857 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) sc(SE)
## scale(SE) -0.001
## scal(dtnSm) 0.119 0.010
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')m <-lmer(scale(idtpSim) ~ scale(NFC) + scale(idtnSim) + ( scale(idtnSim) | subID), data=idSim1)## boundary (singular) fit: see help('isSingular')summary(m)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(idtpSim) ~ scale(NFC) + scale(idtnSim) + (scale(idtnSim) |
## subID)
## Data: idSim1
##
## REML criterion at convergence: 16707
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.4868 -0.6414 -0.1872 0.5156 7.0076
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subID (Intercept) 0.3848073 0.62033
## scale(idtnSim) 0.0006092 0.02468 1.00
## Residual 0.5947457 0.77120
## Number of obs: 6888, groups: subID, 246
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -4.199e-03 4.063e-02 2.426e+02 -0.103 0.91776
## scale(NFC) 1.321e-01 4.006e-02 2.335e+02 3.298 0.00112 **
## scale(idtnSim) 2.109e-02 1.296e-02 3.179e+03 1.627 0.10376
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) s(NFC)
## scale(NFC) 0.001
## scal(dtnSm) 0.124 -0.008
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')m <-lmer(scale(idtpSim) ~ scale(DS) + scale(idtnSim) + ( scale(idtnSim) | subID), data=idSim1)## boundary (singular) fit: see help('isSingular')summary(m)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(idtpSim) ~ scale(DS) + scale(idtnSim) + (scale(idtnSim) |
## subID)
## Data: idSim1
##
## REML criterion at convergence: 16714.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.4825 -0.6426 -0.1918 0.5151 7.0165
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subID (Intercept) 0.3978320 0.63074
## scale(idtnSim) 0.0006567 0.02563 1.00
## Residual 0.5947443 0.77120
## Number of obs: 6888, groups: subID, 246
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -4.369e-03 4.128e-02 2.426e+02 -0.106 0.9158
## scale(DS) -7.053e-02 4.080e-02 2.368e+02 -1.728 0.0852 .
## scale(idtnSim) 2.086e-02 1.298e-02 3.053e+03 1.607 0.1081
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) sc(DS)
## scale(DS) -0.001
## scal(dtnSm) 0.128 0.011
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')m <-lmer(scale(idtpSim) ~ scale(SCC) + scale(idtnSim) + ( scale(idtnSim)| subID), data=idSim1)## boundary (singular) fit: see help('isSingular')summary(m)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(idtpSim) ~ scale(SCC) + scale(idtnSim) + (scale(idtnSim) |
## subID)
## Data: idSim1
##
## REML criterion at convergence: 16716.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.4786 -0.6413 -0.1864 0.5161 7.0170
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subID (Intercept) 0.4005422 0.63288
## scale(idtnSim) 0.0006437 0.02537 1.00
## Residual 0.5947587 0.77121
## Number of obs: 6888, groups: subID, 246
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -4.423e-03 4.141e-02 2.426e+02 -0.107 0.915
## scale(SCC) 4.557e-02 4.092e-02 2.360e+02 1.114 0.267
## scale(idtnSim) 2.107e-02 1.298e-02 3.082e+03 1.623 0.105
##
## Correlation of Fixed Effects:
## (Intr) s(SCC)
## scale(SCC) 0.000
## scal(dtnSm) 0.127 0.003
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')m <-lmer(scale(idtpSim) ~ scale(MemSE) + scale(idtnSim) + ( scale(idtnSim) | subID), data=idSim1)## boundary (singular) fit: see help('isSingular')summary(m)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(idtpSim) ~ scale(MemSE) + scale(idtnSim) + (scale(idtnSim) |
## subID)
## Data: idSim1
##
## REML criterion at convergence: 16715.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.4749 -0.6385 -0.1888 0.5180 7.0187
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subID (Intercept) 0.3986332 0.63137
## scale(idtnSim) 0.0006661 0.02581 1.00
## Residual 0.5947628 0.77121
## Number of obs: 6888, groups: subID, 246
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -4.488e-03 4.131e-02 2.426e+02 -0.109 0.914
## scale(MemSE) 6.233e-02 4.100e-02 2.410e+02 1.520 0.130
## scale(idtnSim) 2.111e-02 1.298e-02 3.042e+03 1.626 0.104
##
## Correlation of Fixed Effects:
## (Intr) s(MSE)
## scale(MmSE) 0.000
## scal(dtnSm) 0.129 0.001
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')m <-lmer(scale(idtpSim) ~ scale(PrivCSE) + scale(idtnSim) + ( scale(idtnSim) | subID), data=idSim1)## boundary (singular) fit: see help('isSingular')summary(m)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(idtpSim) ~ scale(PrivCSE) + scale(idtnSim) + (scale(idtnSim) |
## subID)
## Data: idSim1
##
## REML criterion at convergence: 16714.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.4682 -0.6374 -0.1861 0.5192 7.0167
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subID (Intercept) 0.3965386 0.62971
## scale(idtnSim) 0.0006229 0.02496 1.00
## Residual 0.5947770 0.77122
## Number of obs: 6888, groups: subID, 246
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -4.242e-03 4.121e-02 2.426e+02 -0.103 0.9181
## scale(PrivCSE) 7.509e-02 4.082e-02 2.385e+02 1.839 0.0671 .
## scale(idtnSim) 2.097e-02 1.297e-02 3.153e+03 1.616 0.1061
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) s(PCSE
## scl(PrvCSE) 0.002
## scal(dtnSm) 0.125 -0.006
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')m <-lmer(scale(idtpSim) ~ scale(PubCSE) + scale(idtnSim) + ( scale(idtnSim) | subID), data=idSim1)## boundary (singular) fit: see help('isSingular')summary(m)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(idtpSim) ~ scale(PubCSE) + scale(idtnSim) + (scale(idtnSim) |
## subID)
## Data: idSim1
##
## REML criterion at convergence: 16710.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.4773 -0.6405 -0.1872 0.5150 7.0088
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subID (Intercept) 0.3910845 0.62537
## scale(idtnSim) 0.0007795 0.02792 1.00
## Residual 0.5947460 0.77120
## Number of obs: 6888, groups: subID, 246
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -0.00490 0.04094 242.49877 -0.120 0.90483
## scale(PubCSE) 0.10767 0.04056 239.44539 2.655 0.00847 **
## scale(idtnSim) 0.02139 0.01300 2771.23729 1.646 0.09984 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) s(PCSE
## scal(PbCSE) 0.000
## scal(dtnSm) 0.140 -0.001
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')m <-lmer(scale(idtpSim) ~ scale(MemSE) + scale(idtnSim) + ( scale(idtnSim) | subID), data=idSim1)## boundary (singular) fit: see help('isSingular')summary(m)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(idtpSim) ~ scale(MemSE) + scale(idtnSim) + (scale(idtnSim) |
## subID)
## Data: idSim1
##
## REML criterion at convergence: 16715.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.4749 -0.6385 -0.1888 0.5180 7.0187
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subID (Intercept) 0.3986332 0.63137
## scale(idtnSim) 0.0006661 0.02581 1.00
## Residual 0.5947628 0.77121
## Number of obs: 6888, groups: subID, 246
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -4.488e-03 4.131e-02 2.426e+02 -0.109 0.914
## scale(MemSE) 6.233e-02 4.100e-02 2.410e+02 1.520 0.130
## scale(idtnSim) 2.111e-02 1.298e-02 3.042e+03 1.626 0.104
##
## Correlation of Fixed Effects:
## (Intr) s(MSE)
## scale(MmSE) 0.000
## scal(dtnSm) 0.129 0.001
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')Negative
m <-lmer(scale(idtnSim) ~ scale(SCC) + scale(idtpSim) + ( scale(idtpSim) | subID), data=idSim1)## boundary (singular) fit: see help('isSingular')summary(m)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(idtnSim) ~ scale(SCC) + scale(idtpSim) + (scale(idtpSim) |
## subID)
## Data: idSim1
##
## REML criterion at convergence: 15813.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.2650 -0.5990 -0.1798 0.3468 4.2583
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subID (Intercept) 4.840e-01 0.695729
## scale(idtpSim) 7.144e-06 0.002673 1.00
## Residual 5.158e-01 0.718221
## Number of obs: 6888, groups: subID, 246
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -4.549e-04 4.519e-02 2.426e+02 -0.010 0.992
## scale(SCC) -1.486e-03 4.521e-02 2.428e+02 -0.033 0.974
## scale(idtpSim) 1.454e-02 1.129e-02 5.455e+03 1.288 0.198
##
## Correlation of Fixed Effects:
## (Intr) s(SCC)
## scale(SCC) 0.000
## scal(dtpSm) 0.015 -0.010
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')m <-lmer(scale(idtnSim) ~ scale(Ind) + scale(idtpSim) + ( scale(idtpSim) | subID), data=idSim1)## boundary (singular) fit: see help('isSingular')summary(m)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(idtnSim) ~ scale(Ind) + scale(idtpSim) + (scale(idtpSim) |
## subID)
## Data: idSim1
##
## REML criterion at convergence: 15813.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.2641 -0.5982 -0.1793 0.3462 4.2579
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subID (Intercept) 4.838e-01 0.695593
## scale(idtpSim) 7.559e-06 0.002749 1.00
## Residual 5.158e-01 0.718221
## Number of obs: 6888, groups: subID, 246
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -4.755e-04 4.519e-02 2.425e+02 -0.011 0.992
## scale(Ind) -1.296e-02 4.523e-02 2.433e+02 -0.286 0.775
## scale(idtpSim) 1.466e-02 1.129e-02 5.536e+03 1.298 0.194
##
## Correlation of Fixed Effects:
## (Intr) scl(I)
## scale(Ind) 0.001
## scal(dtpSm) 0.016 -0.039
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')m <-lmer(scale(idtnSim) ~ scale(Inter) + scale(idtpSim) + ( scale(idtpSim) | subID), data=idSim1)## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
## Model failed to converge with max|grad| = 0.00533876 (tol = 0.002, component 1)summary(m)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(idtnSim) ~ scale(Inter) + scale(idtpSim) + (scale(idtpSim) |
## subID)
## Data: idSim1
##
## REML criterion at convergence: 15813.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.2665 -0.5997 -0.1810 0.3469 4.2535
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subID (Intercept) 4.830e-01 0.694979
## scale(idtpSim) 7.996e-06 0.002828 1.00
## Residual 5.158e-01 0.718217
## Number of obs: 6888, groups: subID, 246
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -4.681e-04 4.515e-02 2.426e+02 -0.010 0.992
## scale(Inter) 3.424e-02 4.515e-02 2.420e+02 0.758 0.449
## scale(idtpSim) 1.436e-02 1.129e-02 5.503e+03 1.272 0.204
##
## Correlation of Fixed Effects:
## (Intr) scl(I)
## scale(Intr) 0.000
## scal(dtpSm) 0.016 -0.023
## optimizer (nloptwrap) convergence code: 0 (OK)
## Model failed to converge with max|grad| = 0.00533876 (tol = 0.002, component 1)m <-lmer(scale(idtnSim) ~ scale(SWLS) + scale(idtpSim) + ( scale(idtpSim) | subID), data=idSim1)## boundary (singular) fit: see help('isSingular')summary(m)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(idtnSim) ~ scale(SWLS) + scale(idtpSim) + (scale(idtpSim) |
## subID)
## Data: idSim1
##
## REML criterion at convergence: 15813.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.2654 -0.5992 -0.1798 0.3471 4.2585
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subID (Intercept) 4.840e-01 0.695676
## scale(idtpSim) 6.354e-06 0.002521 1.00
## Residual 5.158e-01 0.718219
## Number of obs: 6888, groups: subID, 246
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -4.249e-04 4.519e-02 2.426e+02 -0.009 0.993
## scale(SWLS) 1.098e-02 4.522e-02 2.431e+02 0.243 0.808
## scale(idtpSim) 1.446e-02 1.129e-02 5.473e+03 1.281 0.200
##
## Correlation of Fixed Effects:
## (Intr) s(SWLS
## scale(SWLS) 0.000
## scal(dtpSm) 0.014 -0.026
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')m <-lmer(scale(idtnSim) ~ scale(IdImp) + scale(idtpSim) + ( scale(idtpSim) | subID), data=idSim1)## boundary (singular) fit: see help('isSingular')summary(m)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(idtnSim) ~ scale(IdImp) + scale(idtpSim) + (scale(idtpSim) |
## subID)
## Data: idSim1
##
## REML criterion at convergence: 15812
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.2633 -0.5996 -0.1774 0.3456 4.2555
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subID (Intercept) 4.803e-01 0.693052
## scale(idtpSim) 3.257e-06 0.001805 1.00
## Residual 5.158e-01 0.718217
## Number of obs: 6888, groups: subID, 246
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -3.034e-04 4.503e-02 2.426e+02 -0.007 0.995
## scale(IdImp) 6.210e-02 4.505e-02 2.431e+02 1.378 0.169
## scale(idtpSim) 1.448e-02 1.128e-02 5.581e+03 1.284 0.199
##
## Correlation of Fixed Effects:
## (Intr) sc(II)
## scal(IdImp) 0.000
## scal(dtpSm) 0.010 -0.007
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')m <-lmer(scale(idtnSim) ~ scale(phi) + scale(idtpSim) + ( scale(idtpSim) | subID), data=idSim1)## boundary (singular) fit: see help('isSingular')summary(m)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(idtnSim) ~ scale(phi) + scale(idtpSim) + (scale(idtpSim) |
## subID)
## Data: idSim1
##
## REML criterion at convergence: 15812.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.2667 -0.6012 -0.1806 0.3483 4.2557
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subID (Intercept) 4.807e-01 0.693293
## scale(idtpSim) 7.805e-06 0.002794 1.00
## Residual 5.158e-01 0.718218
## Number of obs: 6888, groups: subID, 246
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -4.520e-04 4.504e-02 2.427e+02 -0.010 0.992
## scale(phi) -5.882e-02 4.504e-02 2.415e+02 -1.306 0.193
## scale(idtpSim) 1.416e-02 1.129e-02 5.355e+03 1.254 0.210
##
## Correlation of Fixed Effects:
## (Intr) scl(p)
## scale(phi) 0.000
## scal(dtpSm) 0.016 0.029
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')m <-lmer(scale(idtnSim) ~ scale(overlap_norm) + scale(idtpSim) + ( scale(idtpSim) | subID), data=idSim1)## boundary (singular) fit: see help('isSingular')summary(m)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula:
## scale(idtnSim) ~ scale(overlap_norm) + scale(idtpSim) + (scale(idtpSim) |
## subID)
## Data: idSim1
##
## REML criterion at convergence: 15798
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.2469 -0.6015 -0.1822 0.3457 4.2502
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subID (Intercept) 4.536e-01 0.6735281
## scale(idtpSim) 1.252e-07 0.0003538 1.00
## Residual 5.158e-01 0.7181879
## Number of obs: 6888, groups: subID, 246
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -8.580e-06 4.381e-02 2.431e+02 0.000 1.00
## scale(overlap_norm) 1.795e-01 4.436e-02 2.540e+02 4.047 6.9e-05 ***
## scale(idtpSim) 7.880e-03 1.140e-02 6.691e+03 0.691 0.49
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) scl(_)
## scl(vrlp_n) 0.000
## scal(dtpSm) 0.002 -0.155
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')m <-lmer(scale(idtnSim) ~ scale(H_index) + scale(idtpSim) + ( scale(idtpSim) | subID), data=idSim1)## boundary (singular) fit: see help('isSingular')summary(m)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(idtnSim) ~ scale(H_index) + scale(idtpSim) + (scale(idtpSim) |
## subID)
## Data: idSim1
##
## REML criterion at convergence: 15788.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.2425 -0.6031 -0.1772 0.3409 4.2547
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subID (Intercept) 4.348e-01 0.659367
## scale(idtpSim) 1.657e-06 0.001287 1.00
## Residual 5.158e-01 0.718192
## Number of obs: 6888, groups: subID, 246
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -1.969e-05 4.292e-02 2.433e+02 0.000 1.000
## scale(H_index) 2.256e-01 4.335e-02 2.516e+02 5.204 4.06e-07 ***
## scale(idtpSim) 7.461e-03 1.136e-02 5.881e+03 0.657 0.512
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) sc(H_)
## scal(H_ndx) 0.001
## scal(dtpSm) 0.007 -0.134
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')m <-lmer(scale(idtnSim) ~ scale(SE) + scale(idtpSim) + ( scale(idtpSim)| subID), data=idSim1)## boundary (singular) fit: see help('isSingular')summary(m)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(idtnSim) ~ scale(SE) + scale(idtpSim) + (scale(idtpSim) |
## subID)
## Data: idSim1
##
## REML criterion at convergence: 15741.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.2671 -0.5978 -0.1800 0.3433 4.2607
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subID (Intercept) 4.854e-01 0.696722
## scale(idtpSim) 8.092e-06 0.002845 1.00
## Residual 5.151e-01 0.717728
## Number of obs: 6860, groups: subID, 245
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -1.168e-03 4.535e-02 2.415e+02 -0.026 0.979
## scale(SE) -1.841e-02 4.537e-02 2.419e+02 -0.406 0.685
## scale(idtpSim) 1.545e-02 1.131e-02 5.395e+03 1.367 0.172
##
## Correlation of Fixed Effects:
## (Intr) sc(SE)
## scale(SE) 0.000
## scal(dtpSm) 0.016 -0.022
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')m <-lmer(scale(idtnSim) ~ scale(NFC) + scale(idtpSim) + ( scale(idtpSim) | subID), data=idSim1)## boundary (singular) fit: see help('isSingular')summary(m)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(idtnSim) ~ scale(NFC) + scale(idtpSim) + (scale(idtpSim) |
## subID)
## Data: idSim1
##
## REML criterion at convergence: 15813.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.2669 -0.6009 -0.1804 0.3468 4.2628
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subID (Intercept) 4.829e-01 0.694940
## scale(idtpSim) 7.880e-06 0.002807 1.00
## Residual 5.158e-01 0.718220
## Number of obs: 6888, groups: subID, 246
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -4.610e-04 4.514e-02 2.427e+02 -0.010 0.992
## scale(NFC) 3.307e-02 4.519e-02 2.436e+02 0.732 0.465
## scale(idtpSim) 1.428e-02 1.129e-02 5.481e+03 1.265 0.206
##
## Correlation of Fixed Effects:
## (Intr) s(NFC)
## scale(NFC) 0.001
## scal(dtpSm) 0.016 -0.033
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')m <-lmer(scale(idtnSim) ~ scale(DS) + scale(idtpSim) + ( scale(idtpSim) | subID), data=idSim1)## boundary (singular) fit: see help('isSingular')summary(m)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(idtnSim) ~ scale(DS) + scale(idtpSim) + (scale(idtpSim) |
## subID)
## Data: idSim1
##
## REML criterion at convergence: 15813
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.2709 -0.6015 -0.1804 0.3469 4.2619
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subID (Intercept) 4.823e-01 0.69445
## scale(idtpSim) 9.120e-06 0.00302 1.00
## Residual 5.158e-01 0.71822
## Number of obs: 6888, groups: subID, 246
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -5.024e-04 4.511e-02 2.426e+02 -0.011 0.991
## scale(DS) -4.233e-02 4.513e-02 2.430e+02 -0.938 0.349
## scale(idtpSim) 1.438e-02 1.129e-02 5.409e+03 1.274 0.203
##
## Correlation of Fixed Effects:
## (Intr) sc(DS)
## scale(DS) 0.000
## scal(dtpSm) 0.017 0.017
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')m <-lmer(scale(idtnSim) ~ scale(SCC) + scale(idtpSim) + ( scale(idtpSim)| subID), data=idSim1)## boundary (singular) fit: see help('isSingular')summary(m)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(idtnSim) ~ scale(SCC) + scale(idtpSim) + (scale(idtpSim) |
## subID)
## Data: idSim1
##
## REML criterion at convergence: 15813.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.2650 -0.5990 -0.1798 0.3468 4.2583
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subID (Intercept) 4.840e-01 0.695729
## scale(idtpSim) 7.144e-06 0.002673 1.00
## Residual 5.158e-01 0.718221
## Number of obs: 6888, groups: subID, 246
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -4.549e-04 4.519e-02 2.426e+02 -0.010 0.992
## scale(SCC) -1.486e-03 4.521e-02 2.428e+02 -0.033 0.974
## scale(idtpSim) 1.454e-02 1.129e-02 5.455e+03 1.288 0.198
##
## Correlation of Fixed Effects:
## (Intr) s(SCC)
## scale(SCC) 0.000
## scal(dtpSm) 0.015 -0.010
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')m <-lmer(scale(idtnSim) ~ scale(MemSE) + scale(idtpSim) + ( scale(idtpSim) | subID), data=idSim1)## boundary (singular) fit: see help('isSingular')summary(m)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(idtnSim) ~ scale(MemSE) + scale(idtpSim) + (scale(idtpSim) |
## subID)
## Data: idSim1
##
## REML criterion at convergence: 15813.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.2654 -0.5995 -0.1798 0.3463 4.2582
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subID (Intercept) 4.840e-01 0.695711
## scale(idtpSim) 7.586e-06 0.002754 1.00
## Residual 5.158e-01 0.718220
## Number of obs: 6888, groups: subID, 246
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -4.672e-04 4.519e-02 2.426e+02 -0.010 0.992
## scale(MemSE) 5.847e-03 4.522e-02 2.431e+02 0.129 0.897
## scale(idtpSim) 1.451e-02 1.129e-02 5.440e+03 1.286 0.199
##
## Correlation of Fixed Effects:
## (Intr) s(MSE)
## scale(MmSE) 0.000
## scal(dtpSm) 0.016 -0.014
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')m <-lmer(scale(idtnSim) ~ scale(PrivCSE) + scale(idtpSim) + ( scale(idtpSim) | subID), data=idSim1)## boundary (singular) fit: see help('isSingular')summary(m)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(idtnSim) ~ scale(PrivCSE) + scale(idtpSim) + (scale(idtpSim) |
## subID)
## Data: idSim1
##
## REML criterion at convergence: 15812.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.2680 -0.5992 -0.1808 0.3472 4.2532
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subID (Intercept) 4.821e-01 0.694320
## scale(idtpSim) 7.391e-06 0.002719 1.00
## Residual 5.158e-01 0.718219
## Number of obs: 6888, groups: subID, 246
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -4.507e-04 4.511e-02 2.426e+02 -0.010 0.992
## scale(PrivCSE) 4.472e-02 4.511e-02 2.423e+02 0.991 0.322
## scale(idtpSim) 1.435e-02 1.129e-02 5.436e+03 1.272 0.204
##
## Correlation of Fixed Effects:
## (Intr) s(PCSE
## scl(PrvCSE) 0.000
## scal(dtpSm) 0.015 -0.019
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')m <-lmer(scale(idtnSim) ~ scale(PubCSE) + scale(idtpSim) + ( scale(idtpSim) | subID), data=idSim1)## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
## Model failed to converge with max|grad| = 0.00674211 (tol = 0.002, component 1)summary(m)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(idtnSim) ~ scale(PubCSE) + scale(idtpSim) + (scale(idtpSim) |
## subID)
## Data: idSim1
##
## REML criterion at convergence: 15813.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.2653 -0.5991 -0.1797 0.3466 4.2582
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subID (Intercept) 4.841e-01 0.695764
## scale(idtpSim) 7.341e-06 0.002709 1.00
## Residual 5.158e-01 0.718219
## Number of obs: 6888, groups: subID, 246
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -4.596e-04 4.520e-02 2.425e+02 -0.010 0.992
## scale(PubCSE) 1.305e-03 4.522e-02 2.430e+02 0.029 0.977
## scale(idtpSim) 1.453e-02 1.129e-02 5.471e+03 1.287 0.198
##
## Correlation of Fixed Effects:
## (Intr) s(PCSE
## scal(PbCSE) 0.000
## scal(dtpSm) 0.015 -0.025
## optimizer (nloptwrap) convergence code: 0 (OK)
## Model failed to converge with max|grad| = 0.00674211 (tol = 0.002, component 1)m <-lmer(scale(idtnSim) ~ scale(MemSE) + scale(idtpSim) + ( scale(idtpSim) | subID), data=idSim1)## boundary (singular) fit: see help('isSingular')summary(m)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(idtnSim) ~ scale(MemSE) + scale(idtpSim) + (scale(idtpSim) |
## subID)
## Data: idSim1
##
## REML criterion at convergence: 15813.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.2654 -0.5995 -0.1798 0.3463 4.2582
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subID (Intercept) 4.840e-01 0.695711
## scale(idtpSim) 7.586e-06 0.002754 1.00
## Residual 5.158e-01 0.718220
## Number of obs: 6888, groups: subID, 246
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -4.672e-04 4.519e-02 2.426e+02 -0.010 0.992
## scale(MemSE) 5.847e-03 4.522e-02 2.431e+02 0.129 0.897
## scale(idtpSim) 1.451e-02 1.129e-02 5.440e+03 1.286 0.199
##
## Correlation of Fixed Effects:
## (Intr) s(MSE)
## scale(MmSE) 0.000
## scal(dtpSm) 0.016 -0.014
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')Both
m <-lmer(scale(idtSim) ~ scale(SCC) + ( 1 | subID), data=idSim1)
summary(m)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(idtSim) ~ scale(SCC) + (1 | subID)
## Data: idSim1
##
## REML criterion at convergence: 16558.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.2646 -0.6147 -0.1921 0.5184 7.7602
##
## Random effects:
## Groups Name Variance Std.Dev.
## subID (Intercept) 0.4219 0.6495
## Residual 0.5803 0.7618
## Number of obs: 6888, groups: subID, 246
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -7.412e-15 4.242e-02 2.440e+02 0.000 1.000
## scale(SCC) 3.530e-02 4.242e-02 2.440e+02 0.832 0.406
##
## Correlation of Fixed Effects:
## (Intr)
## scale(SCC) 0.000m <-lmer(scale(idtSim) ~ scale(Ind) + ( 1 | subID), data=idSim1)
summary(m)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(idtSim) ~ scale(Ind) + (1 | subID)
## Data: idSim1
##
## REML criterion at convergence: 16545.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.2779 -0.6182 -0.1888 0.5172 7.7610
##
## Random effects:
## Groups Name Variance Std.Dev.
## subID (Intercept) 0.3977 0.6307
## Residual 0.5803 0.7618
## Number of obs: 6888, groups: subID, 246
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -5.741e-15 4.124e-02 2.440e+02 0.000 1.00000
## scale(Ind) 1.588e-01 4.125e-02 2.440e+02 3.851 0.00015 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## scale(Ind) 0.000m <-lmer(scale(idtSim) ~ scale(Inter) + ( 1 | subID), data=idSim1)
summary(m)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(idtSim) ~ scale(Inter) + (1 | subID)
## Data: idSim1
##
## REML criterion at convergence: 16554.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.2693 -0.6133 -0.1927 0.5197 7.7622
##
## Random effects:
## Groups Name Variance Std.Dev.
## subID (Intercept) 0.4137 0.6432
## Residual 0.5803 0.7618
## Number of obs: 6888, groups: subID, 246
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -5.445e-15 4.202e-02 2.440e+02 0.000 1.0000
## scale(Inter) 9.676e-02 4.203e-02 2.440e+02 2.302 0.0222 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## scale(Intr) 0.000m <-lmer(scale(idtSim) ~ scale(SWLS) + ( 1 | subID), data=idSim1)
summary(m)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(idtSim) ~ scale(SWLS) + (1 | subID)
## Data: idSim1
##
## REML criterion at convergence: 16555.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.2661 -0.6148 -0.1935 0.5173 7.7605
##
## Random effects:
## Groups Name Variance Std.Dev.
## subID (Intercept) 0.4156 0.6447
## Residual 0.5803 0.7618
## Number of obs: 6888, groups: subID, 246
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -5.251e-15 4.212e-02 2.440e+02 0.000 1.0000
## scale(SWLS) 8.645e-02 4.212e-02 2.440e+02 2.053 0.0412 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## scale(SWLS) 0.000m <-lmer(scale(idtSim) ~ scale(IdImp) + ( 1 | subID), data=idSim1)
summary(m)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(idtSim) ~ scale(IdImp) + (1 | subID)
## Data: idSim1
##
## REML criterion at convergence: 16559.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.2607 -0.6126 -0.1920 0.5190 7.7619
##
## Random effects:
## Groups Name Variance Std.Dev.
## subID (Intercept) 0.4228 0.6502
## Residual 0.5803 0.7618
## Number of obs: 6888, groups: subID, 246
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -8.017e-15 4.246e-02 2.440e+02 0.000 1.000
## scale(IdImp) 1.843e-02 4.247e-02 2.440e+02 0.434 0.665
##
## Correlation of Fixed Effects:
## (Intr)
## scal(IdImp) 0.000m <-lmer(scale(idtSim) ~ scale(phi) + ( 1 | subID), data=idSim1)
summary(m)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(idtSim) ~ scale(phi) + (1 | subID)
## Data: idSim1
##
## REML criterion at convergence: 16555
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.2605 -0.6137 -0.1879 0.5196 7.7573
##
## Random effects:
## Groups Name Variance Std.Dev.
## subID (Intercept) 0.4150 0.6442
## Residual 0.5803 0.7618
## Number of obs: 6888, groups: subID, 246
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -7.702e-15 4.209e-02 2.440e+02 0.000 1.0000
## scale(phi) -8.981e-02 4.209e-02 2.440e+02 -2.134 0.0339 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## scale(phi) 0.000m <-lmer(scale(idtSim) ~ scale(overlap_norm) + ( 1 | subID), data=idSim1)
summary(m)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(idtSim) ~ scale(overlap_norm) + (1 | subID)
## Data: idSim1
##
## REML criterion at convergence: 16011.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.6606 -0.6123 -0.1771 0.5399 7.8079
##
## Random effects:
## Groups Name Variance Std.Dev.
## subID (Intercept) 0.02633 0.1623
## Residual 0.58030 0.7618
## Number of obs: 6888, groups: subID, 246
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -9.045e-16 1.383e-02 2.440e+02 0.00 1
## scale(overlap_norm) 6.274e-01 1.383e-02 2.440e+02 45.36 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## scl(vrlp_n) 0.000m <-lmer(scale(idtSim) ~ scale(H_index) + ( 1 | subID), data=idSim1)
summary(m)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(idtSim) ~ scale(H_index) + (1 | subID)
## Data: idSim1
##
## REML criterion at convergence: 16340.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.0934 -0.5980 -0.1875 0.5102 7.8369
##
## Random effects:
## Groups Name Variance Std.Dev.
## subID (Intercept) 0.1600 0.4000
## Residual 0.5803 0.7618
## Number of obs: 6888, groups: subID, 246
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -4.486e-15 2.711e-02 2.440e+02 0.00 1
## scale(H_index) 5.109e-01 2.711e-02 2.440e+02 18.85 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## scal(H_ndx) 0.000m <-lmer(scale(idtSim) ~ scale(SE) + ( 1| subID), data=idSim1)
summary(m)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(idtSim) ~ scale(SE) + (1 | subID)
## Data: idSim1
##
## REML criterion at convergence: 16496.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.2653 -0.6157 -0.1894 0.5188 7.7542
##
## Random effects:
## Groups Name Variance Std.Dev.
## subID (Intercept) 0.4167 0.6455
## Residual 0.5809 0.7622
## Number of obs: 6860, groups: subID, 245
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 2.734e-03 4.226e-02 2.430e+02 0.065 0.9485
## scale(SE) 7.968e-02 4.226e-02 2.430e+02 1.885 0.0606 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## scale(SE) 0.000m <-lmer(scale(idtSim) ~ scale(NFC) + ( 1 | subID), data=idSim1)
summary(m)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(idtSim) ~ scale(NFC) + (1 | subID)
## Data: idSim1
##
## REML criterion at convergence: 16551.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.2728 -0.6170 -0.1912 0.5182 7.7561
##
## Random effects:
## Groups Name Variance Std.Dev.
## subID (Intercept) 0.4081 0.6388
## Residual 0.5803 0.7618
## Number of obs: 6888, groups: subID, 246
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -4.217e-15 4.175e-02 2.440e+02 0.000 1.00000
## scale(NFC) 1.223e-01 4.175e-02 2.440e+02 2.929 0.00372 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## scale(NFC) 0.000m <-lmer(scale(idtSim) ~ scale(DS) + ( 1 | subID), data=idSim1)
summary(m)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(idtSim) ~ scale(DS) + (1 | subID)
## Data: idSim1
##
## REML criterion at convergence: 16557.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.2683 -0.6150 -0.1919 0.5192 7.7594
##
## Random effects:
## Groups Name Variance Std.Dev.
## subID (Intercept) 0.4192 0.6475
## Residual 0.5803 0.7618
## Number of obs: 6888, groups: subID, 246
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -5.432e-15 4.229e-02 2.440e+02 0.00 1.00
## scale(DS) -6.260e-02 4.229e-02 2.440e+02 -1.48 0.14
##
## Correlation of Fixed Effects:
## (Intr)
## scale(DS) 0.000m <-lmer(scale(idtSim) ~ scale(SCC) + ( 1| subID), data=idSim1)
summary(m)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(idtSim) ~ scale(SCC) + (1 | subID)
## Data: idSim1
##
## REML criterion at convergence: 16558.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.2646 -0.6147 -0.1921 0.5184 7.7602
##
## Random effects:
## Groups Name Variance Std.Dev.
## subID (Intercept) 0.4219 0.6495
## Residual 0.5803 0.7618
## Number of obs: 6888, groups: subID, 246
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -7.412e-15 4.242e-02 2.440e+02 0.000 1.000
## scale(SCC) 3.530e-02 4.242e-02 2.440e+02 0.832 0.406
##
## Correlation of Fixed Effects:
## (Intr)
## scale(SCC) 0.000m <-lmer(scale(idtSim) ~ scale(MemSE) + ( 1 | subID), data=idSim1)
summary(m)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(idtSim) ~ scale(MemSE) + (1 | subID)
## Data: idSim1
##
## REML criterion at convergence: 16558.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.2624 -0.6133 -0.1924 0.5193 7.7627
##
## Random effects:
## Groups Name Variance Std.Dev.
## subID (Intercept) 0.4205 0.6485
## Residual 0.5803 0.7618
## Number of obs: 6888, groups: subID, 246
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -6.489e-15 4.235e-02 2.440e+02 0.000 1.00
## scale(MemSE) 5.099e-02 4.236e-02 2.440e+02 1.204 0.23
##
## Correlation of Fixed Effects:
## (Intr)
## scale(MmSE) 0.000m <-lmer(scale(idtSim) ~ scale(PrivCSE) + ( 1 | subID), data=idSim1)
summary(m)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(idtSim) ~ scale(PrivCSE) + (1 | subID)
## Data: idSim1
##
## REML criterion at convergence: 16555.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.2557 -0.6146 -0.1910 0.5213 7.7609
##
## Random effects:
## Groups Name Variance Std.Dev.
## subID (Intercept) 0.4161 0.6450
## Residual 0.5803 0.7618
## Number of obs: 6888, groups: subID, 246
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -8.270e-15 4.214e-02 2.440e+02 0.00 1.0000
## scale(PrivCSE) 8.384e-02 4.214e-02 2.440e+02 1.99 0.0478 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## scl(PrvCSE) 0.000m <-lmer(scale(idtSim) ~ scale(PubCSE) + ( 1 | subID), data=idSim1)
summary(m)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(idtSim) ~ scale(PubCSE) + (1 | subID)
## Data: idSim1
##
## REML criterion at convergence: 16554.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.2656 -0.6131 -0.1904 0.5211 7.7600
##
## Random effects:
## Groups Name Variance Std.Dev.
## subID (Intercept) 0.4135 0.6431
## Residual 0.5803 0.7618
## Number of obs: 6888, groups: subID, 246
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -7.784e-15 4.201e-02 2.440e+02 0.000 1.0000
## scale(PubCSE) 9.779e-02 4.202e-02 2.440e+02 2.327 0.0208 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## scal(PbCSE) 0.000m <-lmer(scale(idtSim) ~ scale(MemSE) + ( 1 | subID), data=idSim1)
summary(m)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(idtSim) ~ scale(MemSE) + (1 | subID)
## Data: idSim1
##
## REML criterion at convergence: 16558.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.2624 -0.6133 -0.1924 0.5193 7.7627
##
## Random effects:
## Groups Name Variance Std.Dev.
## subID (Intercept) 0.4205 0.6485
## Residual 0.5803 0.7618
## Number of obs: 6888, groups: subID, 246
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -6.489e-15 4.235e-02 2.440e+02 0.000 1.00
## scale(MemSE) 5.099e-02 4.236e-02 2.440e+02 1.204 0.23
##
## Correlation of Fixed Effects:
## (Intr)
## scale(MmSE) 0.000Individual Differences Predict Pairwise Overlap in Identities in Common
m <-lmer(scale(idSim) ~ scale(SCC) + ( 1 | subID), data=idSim1)
summary(m)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(idSim) ~ scale(SCC) + (1 | subID)
## Data: idSim1
##
## REML criterion at convergence: 15808.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.2437 -0.6057 -0.1938 0.3497 4.2370
##
## Random effects:
## Groups Name Variance Std.Dev.
## subID (Intercept) 0.4882 0.6987
## Residual 0.5157 0.7181
## Number of obs: 6888, groups: subID, 246
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 4.883e-15 4.538e-02 2.440e+02 0.000 1.000
## scale(SCC) -1.252e-03 4.539e-02 2.440e+02 -0.028 0.978
##
## Correlation of Fixed Effects:
## (Intr)
## scale(SCC) 0.000m <-lmer(scale(idSim) ~ scale(Ind) + ( 1 | subID), data=idSim1)
summary(m)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(idSim) ~ scale(Ind) + (1 | subID)
## Data: idSim1
##
## REML criterion at convergence: 15808.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.2427 -0.6055 -0.1928 0.3496 4.2380
##
## Random effects:
## Groups Name Variance Std.Dev.
## subID (Intercept) 0.4881 0.6987
## Residual 0.5157 0.7181
## Number of obs: 6888, groups: subID, 246
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 9.267e-15 4.538e-02 2.440e+02 0.000 1.000
## scale(Ind) -1.030e-02 4.538e-02 2.440e+02 -0.227 0.821
##
## Correlation of Fixed Effects:
## (Intr)
## scale(Ind) 0.000m <-lmer(scale(idSim) ~ scale(Inter) + ( 1 | subID), data=idSim1)
summary(m)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(idSim) ~ scale(Inter) + (1 | subID)
## Data: idSim1
##
## REML criterion at convergence: 15807.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.2452 -0.6055 -0.1906 0.3462 4.2403
##
## Random effects:
## Groups Name Variance Std.Dev.
## subID (Intercept) 0.4870 0.6978
## Residual 0.5157 0.7181
## Number of obs: 6888, groups: subID, 246
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 7.909e-15 4.533e-02 2.440e+02 0.000 1.000
## scale(Inter) 3.543e-02 4.533e-02 2.440e+02 0.782 0.435
##
## Correlation of Fixed Effects:
## (Intr)
## scale(Intr) 0.000m <-lmer(scale(idSim) ~ scale(SWLS) + ( 1 | subID), data=idSim1)
summary(m)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(idSim) ~ scale(SWLS) + (1 | subID)
## Data: idSim1
##
## REML criterion at convergence: 15808.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.2447 -0.6058 -0.1927 0.3491 4.2381
##
## Random effects:
## Groups Name Variance Std.Dev.
## subID (Intercept) 0.4881 0.6986
## Residual 0.5157 0.7181
## Number of obs: 6888, groups: subID, 246
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 8.683e-15 4.537e-02 2.440e+02 0.000 1.000
## scale(SWLS) 1.320e-02 4.538e-02 2.440e+02 0.291 0.771
##
## Correlation of Fixed Effects:
## (Intr)
## scale(SWLS) 0.000m <-lmer(scale(idSim) ~ scale(IdImp) + ( 1 | subID), data=idSim1)
summary(m)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(idSim) ~ scale(IdImp) + (1 | subID)
## Data: idSim1
##
## REML criterion at convergence: 15806.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.2438 -0.6058 -0.1869 0.3510 4.2439
##
## Random effects:
## Groups Name Variance Std.Dev.
## subID (Intercept) 0.4842 0.6959
## Residual 0.5157 0.7181
## Number of obs: 6888, groups: subID, 246
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 4.354e-15 4.520e-02 2.440e+02 0.000 1.000
## scale(IdImp) 6.303e-02 4.521e-02 2.440e+02 1.394 0.165
##
## Correlation of Fixed Effects:
## (Intr)
## scal(IdImp) 0.000m <-lmer(scale(idSim) ~ scale(phi) + ( 1 | subID), data=idSim1)
summary(m)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(idSim) ~ scale(phi) + (1 | subID)
## Data: idSim1
##
## REML criterion at convergence: 15806.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.2458 -0.6070 -0.1889 0.3487 4.2419
##
## Random effects:
## Groups Name Variance Std.Dev.
## subID (Intercept) 0.4846 0.6961
## Residual 0.5157 0.7181
## Number of obs: 6888, groups: subID, 246
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 7.393e-15 4.522e-02 2.440e+02 0.000 1.000
## scale(phi) -6.035e-02 4.522e-02 2.440e+02 -1.335 0.183
##
## Correlation of Fixed Effects:
## (Intr)
## scale(phi) 0.000m <-lmer(scale(idSim) ~ scale(overlap_norm) + ( 1 | subID), data=idSim1)
summary(m)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(idSim) ~ scale(overlap_norm) + (1 | subID)
## Data: idSim1
##
## REML criterion at convergence: 15791.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.2373 -0.6025 -0.1833 0.3453 4.2475
##
## Random effects:
## Groups Name Variance Std.Dev.
## subID (Intercept) 0.4540 0.6738
## Residual 0.5157 0.7181
## Number of obs: 6888, groups: subID, 246
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 4.184e-15 4.382e-02 2.440e+02 0.000 1
## scale(overlap_norm) 1.843e-01 4.382e-02 2.440e+02 4.206 3.65e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## scl(vrlp_n) 0.000m <-lmer(scale(idSim) ~ scale(H_index) + ( 1 | subID), data=idSim1)
summary(m)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(idSim) ~ scale(H_index) + (1 | subID)
## Data: idSim1
##
## REML criterion at convergence: 15781.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.2312 -0.6063 -0.1814 0.3389 4.2524
##
## Random effects:
## Groups Name Variance Std.Dev.
## subID (Intercept) 0.4352 0.6597
## Residual 0.5157 0.7181
## Number of obs: 6888, groups: subID, 246
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 5.340e-15 4.294e-02 2.440e+02 0.000 1
## scale(H_index) 2.294e-01 4.294e-02 2.440e+02 5.342 2.11e-07 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## scal(H_ndx) 0.000m <-lmer(scale(idSim) ~ scale(SE) + ( 1| subID), data=idSim1)
summary(m)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(idSim) ~ scale(SE) + (1 | subID)
## Data: idSim1
##
## REML criterion at convergence: 15736
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.2444 -0.6061 -0.1920 0.3399 4.2418
##
## Random effects:
## Groups Name Variance Std.Dev.
## subID (Intercept) 0.490 0.7000
## Residual 0.515 0.7177
## Number of obs: 6860, groups: subID, 245
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -6.381e-04 4.555e-02 2.430e+02 -0.014 0.989
## scale(SE) -1.679e-02 4.555e-02 2.430e+02 -0.369 0.713
##
## Correlation of Fixed Effects:
## (Intr)
## scale(SE) 0.000m <-lmer(scale(idSim) ~ scale(NFC) + ( 1 | subID), data=idSim1)
summary(m)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(idSim) ~ scale(NFC) + (1 | subID)
## Data: idSim1
##
## REML criterion at convergence: 15807.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.2458 -0.6076 -0.1894 0.3492 4.2414
##
## Random effects:
## Groups Name Variance Std.Dev.
## subID (Intercept) 0.4870 0.6979
## Residual 0.5157 0.7181
## Number of obs: 6888, groups: subID, 246
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 8.478e-15 4.533e-02 2.440e+02 0.000 1.000
## scale(NFC) 3.482e-02 4.533e-02 2.440e+02 0.768 0.443
##
## Correlation of Fixed Effects:
## (Intr)
## scale(NFC) 0.000m <-lmer(scale(idSim) ~ scale(DS) + ( 1 | subID), data=idSim1)
summary(m)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(idSim) ~ scale(DS) + (1 | subID)
## Data: idSim1
##
## REML criterion at convergence: 15807.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.2496 -0.6042 -0.1896 0.3510 4.2412
##
## Random effects:
## Groups Name Variance Std.Dev.
## subID (Intercept) 0.4864 0.6974
## Residual 0.5157 0.7181
## Number of obs: 6888, groups: subID, 246
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 1.022e-14 4.530e-02 2.440e+02 0.000 1.000
## scale(DS) -4.289e-02 4.530e-02 2.440e+02 -0.947 0.345
##
## Correlation of Fixed Effects:
## (Intr)
## scale(DS) 0.000m <-lmer(scale(idSim) ~ scale(SCC) + ( 1| subID), data=idSim1)
summary(m)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(idSim) ~ scale(SCC) + (1 | subID)
## Data: idSim1
##
## REML criterion at convergence: 15808.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.2437 -0.6057 -0.1938 0.3497 4.2370
##
## Random effects:
## Groups Name Variance Std.Dev.
## subID (Intercept) 0.4882 0.6987
## Residual 0.5157 0.7181
## Number of obs: 6888, groups: subID, 246
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 4.883e-15 4.538e-02 2.440e+02 0.000 1.000
## scale(SCC) -1.252e-03 4.539e-02 2.440e+02 -0.028 0.978
##
## Correlation of Fixed Effects:
## (Intr)
## scale(SCC) 0.000m <-lmer(scale(idSim) ~ scale(MemSE) + ( 1 | subID), data=idSim1)
summary(m)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(idSim) ~ scale(MemSE) + (1 | subID)
## Data: idSim1
##
## REML criterion at convergence: 15808.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.2440 -0.6057 -0.1932 0.3499 4.2376
##
## Random effects:
## Groups Name Variance Std.Dev.
## subID (Intercept) 0.4882 0.6987
## Residual 0.5157 0.7181
## Number of obs: 6888, groups: subID, 246
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 9.275e-15 4.538e-02 2.440e+02 0.000 1.000
## scale(MemSE) 6.208e-03 4.538e-02 2.440e+02 0.137 0.891
##
## Correlation of Fixed Effects:
## (Intr)
## scale(MmSE) 0.000m <-lmer(scale(idSim) ~ scale(PrivCSE) + ( 1 | subID), data=idSim1)
summary(m)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(idSim) ~ scale(PrivCSE) + (1 | subID)
## Data: idSim1
##
## REML criterion at convergence: 15807.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.2471 -0.6060 -0.1898 0.3511 4.2410
##
## Random effects:
## Groups Name Variance Std.Dev.
## subID (Intercept) 0.4861 0.6972
## Residual 0.5157 0.7181
## Number of obs: 6888, groups: subID, 246
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 1.105e-14 4.529e-02 2.440e+02 0.000 1.000
## scale(PrivCSE) 4.581e-02 4.529e-02 2.440e+02 1.011 0.313
##
## Correlation of Fixed Effects:
## (Intr)
## scl(PrvCSE) 0.000m <-lmer(scale(idSim) ~ scale(PubCSE) + ( 1 | subID), data=idSim1)
summary(m)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(idSim) ~ scale(PubCSE) + (1 | subID)
## Data: idSim1
##
## REML criterion at convergence: 15808.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.2440 -0.6057 -0.1939 0.3497 4.2369
##
## Random effects:
## Groups Name Variance Std.Dev.
## subID (Intercept) 0.4882 0.6987
## Residual 0.5157 0.7181
## Number of obs: 6888, groups: subID, 246
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 5.889e-15 4.538e-02 2.440e+02 0.000 1.000
## scale(PubCSE) 2.111e-03 4.539e-02 2.440e+02 0.047 0.963
##
## Correlation of Fixed Effects:
## (Intr)
## scal(PbCSE) 0.000m <-lmer(scale(idSim) ~ scale(MemSE) + ( 1 | subID), data=idSim1)
summary(m)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(idSim) ~ scale(MemSE) + (1 | subID)
## Data: idSim1
##
## REML criterion at convergence: 15808.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.2440 -0.6057 -0.1932 0.3499 4.2376
##
## Random effects:
## Groups Name Variance Std.Dev.
## subID (Intercept) 0.4882 0.6987
## Residual 0.5157 0.7181
## Number of obs: 6888, groups: subID, 246
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 9.275e-15 4.538e-02 2.440e+02 0.000 1.000
## scale(MemSE) 6.208e-03 4.538e-02 2.440e+02 0.137 0.891
##
## Correlation of Fixed Effects:
## (Intr)
## scale(MmSE) 0.000Study 2
Positive
m <-lmer(scale(idtpSim) ~ scale(SCC) + scale(idtnSim) + ( scale(idtnSim) | subID), data=idSim2)
summary(m)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(idtpSim) ~ scale(SCC) + scale(idtnSim) + (scale(idtnSim) |
## subID)
## Data: idSim2
##
## REML criterion at convergence: 16875.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.4297 -0.6105 -0.2583 0.4996 6.8372
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subID (Intercept) 0.37363 0.6113
## scale(idtnSim) 0.01323 0.1150 0.13
## Residual 0.59745 0.7729
## Number of obs: 6916, groups: subID, 247
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -0.004870 0.040195 239.991833 -0.121 0.903673
## scale(SCC) -0.007779 0.040025 231.013660 -0.194 0.846062
## scale(idtnSim) 0.048476 0.014208 245.034120 3.412 0.000754 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) s(SCC)
## scale(SCC) 0.000
## scal(dtnSm) 0.068 0.010m <-lmer(scale(idtpSim) ~ scale(Ind) + scale(idtnSim) + ( scale(idtnSim) | subID), data=idSim2)
summary(m)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(idtpSim) ~ scale(Ind) + scale(idtnSim) + (scale(idtnSim) |
## subID)
## Data: idSim2
##
## REML criterion at convergence: 16875.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.4303 -0.6112 -0.2576 0.4998 6.8381
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subID (Intercept) 0.37362 0.6112
## scale(idtnSim) 0.01323 0.1150 0.13
## Residual 0.59744 0.7729
## Number of obs: 6916, groups: subID, 247
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -0.004853 0.040195 239.953560 -0.121 0.904005
## scale(Ind) 0.006731 0.040008 230.700716 0.168 0.866543
## scale(idtnSim) 0.048523 0.014208 245.017466 3.415 0.000746 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) scl(I)
## scale(Ind) -0.001
## scal(dtnSm) 0.067 0.009m <-lmer(scale(idtpSim) ~ scale(Inter) + scale(idtnSim) + ( scale(idtnSim) | subID), data=idSim2)
summary(m)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(idtpSim) ~ scale(Inter) + scale(idtnSim) + (scale(idtnSim) |
## subID)
## Data: idSim2
##
## REML criterion at convergence: 16875.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.4297 -0.6116 -0.2578 0.5004 6.8337
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subID (Intercept) 0.37315 0.6109
## scale(idtnSim) 0.01319 0.1148 0.13
## Residual 0.59746 0.7730
## Number of obs: 6916, groups: subID, 247
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -0.004877 0.040171 240.078598 -0.121 0.90347
## scale(Inter) 0.024944 0.040110 236.395194 0.622 0.53461
## scale(idtnSim) 0.048525 0.014200 245.126239 3.417 0.00074 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) scl(I)
## scale(Intr) -0.001
## scal(dtnSm) 0.067 0.001m <-lmer(scale(idtpSim) ~ scale(SWLS) + scale(idtnSim) + ( scale(idtnSim) | subID), data=idSim2)
summary(m)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(idtpSim) ~ scale(SWLS) + scale(idtnSim) + (scale(idtnSim) |
## subID)
## Data: idSim2
##
## REML criterion at convergence: 16875.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.4301 -0.6107 -0.2591 0.4990 6.8356
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subID (Intercept) 0.37334 0.6110
## scale(idtnSim) 0.01321 0.1150 0.13
## Residual 0.59745 0.7729
## Number of obs: 6916, groups: subID, 247
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -0.004816 0.040181 239.936277 -0.120 0.904692
## scale(SWLS) -0.018150 0.040073 234.198236 -0.453 0.651018
## scale(idtnSim) 0.048572 0.014205 244.958608 3.419 0.000735 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) s(SWLS
## scale(SWLS) 0.001
## scal(dtnSm) 0.065 -0.010m <-lmer(scale(idtpSim) ~ scale(IdImp) + scale(idtnSim) + ( scale(idtnSim) | subID), data=idSim2)
summary(m)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(idtpSim) ~ scale(IdImp) + scale(idtnSim) + (scale(idtnSim) |
## subID)
## Data: idSim2
##
## REML criterion at convergence: 16872
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.4335 -0.6122 -0.2593 0.5003 6.8393
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subID (Intercept) 0.36767 0.6064
## scale(idtnSim) 0.01333 0.1154 0.12
## Residual 0.59739 0.7729
## Number of obs: 6916, groups: subID, 247
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -0.004651 0.039896 239.835555 -0.117 0.907301
## scale(IdImp) 0.076797 0.039746 232.098472 1.932 0.054549 .
## scale(idtnSim) 0.048393 0.014220 244.624809 3.403 0.000778 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) sc(II)
## scal(IdImp) 0.001
## scal(dtnSm) 0.061 -0.010m <-lmer(scale(idtpSim) ~ scale(phi) + scale(idtnSim) + ( scale(idtnSim) | subID), data=idSim2)
summary(m)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(idtpSim) ~ scale(phi) + scale(idtnSim) + (scale(idtnSim) |
## subID)
## Data: idSim2
##
## REML criterion at convergence: 16867.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.4425 -0.6128 -0.2607 0.4968 6.8570
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subID (Intercept) 0.36127 0.6011
## scale(idtnSim) 0.01304 0.1142 0.10
## Residual 0.59747 0.7730
## Number of obs: 6916, groups: subID, 247
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -0.004135 0.039568 239.928621 -0.105 0.916851
## scale(phi) -0.112466 0.039461 233.395432 -2.850 0.004762 **
## scale(idtnSim) 0.047844 0.014174 245.777641 3.375 0.000856 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) scl(p)
## scale(phi) -0.002
## scal(dtnSm) 0.051 0.025m <-lmer(scale(idtpSim) ~ scale(overlap_norm) + scale(idtnSim) + ( scale(idtnSim) | subID), data=idSim2)
summary(m)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula:
## scale(idtpSim) ~ scale(overlap_norm) + scale(idtnSim) + (scale(idtnSim) |
## subID)
## Data: idSim2
##
## REML criterion at convergence: 16431.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.3279 -0.6247 -0.2276 0.5101 6.7841
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subID (Intercept) 0.04302 0.2074
## scale(idtnSim) 0.01085 0.1042 -0.36
## Residual 0.59799 0.7733
## Number of obs: 6916, groups: subID, 247
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 0.00154 0.01656 243.05209 0.093 0.925988
## scale(overlap_norm) 0.58420 0.01618 225.57025 36.109 < 2e-16 ***
## scale(idtnSim) 0.04391 0.01288 279.25252 3.409 0.000748 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) scl(_)
## scl(vrlp_n) -0.003
## scal(dtnSm) -0.156 -0.119m <-lmer(scale(idtpSim) ~ scale(H_index) + scale(idtnSim) + ( scale(idtnSim) | subID), data=idSim2)
summary(m)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(idtpSim) ~ scale(H_index) + scale(idtnSim) + (scale(idtnSim) |
## subID)
## Data: idSim2
##
## REML criterion at convergence: 16691.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.2376 -0.6187 -0.2504 0.4861 6.8250
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subID (Intercept) 0.16524 0.4065
## scale(idtnSim) 0.01323 0.1150 -0.09
## Residual 0.59706 0.7727
## Number of obs: 6916, groups: subID, 247
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -0.001734 0.027790 239.474808 -0.062 0.95031
## scale(H_index) 0.463588 0.027751 245.282449 16.705 < 2e-16 ***
## scale(idtnSim) 0.044565 0.013990 255.211824 3.186 0.00162 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) sc(H_)
## scal(H_ndx) 0.000
## scal(dtnSm) -0.045 -0.070m <-lmer(scale(idtpSim) ~ scale(SE) + scale(idtnSim) + ( scale(idtnSim)| subID), data=idSim2)
summary(m)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(idtpSim) ~ scale(SE) + scale(idtnSim) + (scale(idtnSim) |
## subID)
## Data: idSim2
##
## REML criterion at convergence: 16875.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.4299 -0.6122 -0.2592 0.4999 6.8406
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subID (Intercept) 0.37304 0.6108
## scale(idtnSim) 0.01323 0.1150 0.13
## Residual 0.59745 0.7729
## Number of obs: 6916, groups: subID, 247
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -0.004892 0.040165 239.937004 -0.122 0.903160
## scale(SE) 0.024422 0.040004 230.805493 0.611 0.542129
## scale(idtnSim) 0.048521 0.014207 245.069511 3.415 0.000746 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) sc(SE)
## scale(SE) 0.001
## scal(dtnSm) 0.069 0.000m <-lmer(scale(idtpSim) ~ scale(NFC) + scale(idtnSim) + ( scale(idtnSim) | subID), data=idSim2)
summary(m)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(idtpSim) ~ scale(NFC) + scale(idtnSim) + (scale(idtnSim) |
## subID)
## Data: idSim2
##
## REML criterion at convergence: 16875.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.4297 -0.6105 -0.2577 0.4995 6.8385
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subID (Intercept) 0.37348 0.6111
## scale(idtnSim) 0.01324 0.1151 0.13
## Residual 0.59744 0.7729
## Number of obs: 6916, groups: subID, 247
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -0.004868 0.040188 239.944596 -0.121 0.903684
## scale(NFC) 0.012812 0.040011 230.989555 0.320 0.749091
## scale(idtnSim) 0.048547 0.014209 244.958441 3.417 0.000742 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) s(NFC)
## scale(NFC) -0.001
## scal(dtnSm) 0.067 0.009m <-lmer(scale(idtpSim) ~ scale(DS) + scale(idtnSim) + ( scale(idtnSim) | subID), data=idSim2)
summary(m)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(idtpSim) ~ scale(DS) + scale(idtnSim) + (scale(idtnSim) |
## subID)
## Data: idSim2
##
## REML criterion at convergence: 16874
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.4301 -0.6114 -0.2613 0.5004 6.8282
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subID (Intercept) 0.37115 0.6092
## scale(idtnSim) 0.01313 0.1146 0.12
## Residual 0.59747 0.7730
## Number of obs: 6916, groups: subID, 247
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -0.004753 0.040070 240.094424 -0.119 0.905673
## scale(DS) 0.051613 0.039964 233.544409 1.291 0.197810
## scale(idtnSim) 0.048425 0.014191 245.402535 3.412 0.000753 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) sc(DS)
## scale(DS) 0.000
## scal(dtnSm) 0.063 -0.008m <-lmer(scale(idtpSim) ~ scale(SCC) + scale(idtnSim) + ( scale(idtnSim)| subID), data=idSim2)
summary(m)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(idtpSim) ~ scale(SCC) + scale(idtnSim) + (scale(idtnSim) |
## subID)
## Data: idSim2
##
## REML criterion at convergence: 16875.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.4297 -0.6105 -0.2583 0.4996 6.8372
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subID (Intercept) 0.37363 0.6113
## scale(idtnSim) 0.01323 0.1150 0.13
## Residual 0.59745 0.7729
## Number of obs: 6916, groups: subID, 247
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -0.004870 0.040195 239.991833 -0.121 0.903673
## scale(SCC) -0.007779 0.040025 231.013660 -0.194 0.846062
## scale(idtnSim) 0.048476 0.014208 245.034120 3.412 0.000754 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) s(SCC)
## scale(SCC) 0.000
## scal(dtnSm) 0.068 0.010m <-lmer(scale(idtpSim) ~ scale(MemSE) + scale(idtnSim) + ( scale(idtnSim) | subID), data=idSim2)
summary(m)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(idtpSim) ~ scale(MemSE) + scale(idtnSim) + (scale(idtnSim) |
## subID)
## Data: idSim2
##
## REML criterion at convergence: 16874.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.4250 -0.6120 -0.2553 0.5019 6.8431
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subID (Intercept) 0.37149 0.6095
## scale(idtnSim) 0.01326 0.1152 0.13
## Residual 0.59743 0.7729
## Number of obs: 6916, groups: subID, 247
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -0.004925 0.040088 239.919466 -0.123 0.902315
## scale(MemSE) 0.045983 0.039932 232.691863 1.152 0.250699
## scale(idtnSim) 0.048829 0.014213 244.906886 3.436 0.000694 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) s(MSE)
## scale(MmSE) -0.001
## scal(dtnSm) 0.068 0.016m <-lmer(scale(idtpSim) ~ scale(PrivCSE) + scale(idtnSim) + ( scale(idtnSim) | subID), data=idSim2)
summary(m)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(idtpSim) ~ scale(PrivCSE) + scale(idtnSim) + (scale(idtnSim) |
## subID)
## Data: idSim2
##
## REML criterion at convergence: 16873.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.4292 -0.6132 -0.2561 0.4995 6.8420
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subID (Intercept) 0.37046 0.6087
## scale(idtnSim) 0.01323 0.1150 0.12
## Residual 0.59744 0.7729
## Number of obs: 6916, groups: subID, 247
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -0.004921 0.040036 239.950353 -0.123 0.902282
## scale(PrivCSE) 0.056022 0.039887 232.377855 1.405 0.161505
## scale(idtnSim) 0.049049 0.014209 245.251014 3.452 0.000655 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) s(PCSE
## scl(PrvCSE) -0.002
## scal(dtnSm) 0.064 0.023m <-lmer(scale(idtpSim) ~ scale(PubCSE) + scale(idtnSim) + ( scale(idtnSim) | subID), data=idSim2)
summary(m)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(idtpSim) ~ scale(PubCSE) + scale(idtnSim) + (scale(idtnSim) |
## subID)
## Data: idSim2
##
## REML criterion at convergence: 16875.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.4303 -0.6131 -0.2571 0.4994 6.8387
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subID (Intercept) 0.37337 0.6110
## scale(idtnSim) 0.01321 0.1149 0.13
## Residual 0.59745 0.7729
## Number of obs: 6916, groups: subID, 247
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -0.004809 0.040183 239.944168 -0.120 0.904842
## scale(PubCSE) 0.017806 0.039999 231.162936 0.445 0.656623
## scale(idtnSim) 0.048547 0.014205 245.113542 3.418 0.000739 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) s(PCSE
## scal(PbCSE) 0.000
## scal(dtnSm) 0.066 0.007m <-lmer(scale(idtpSim) ~ scale(MemSE) + scale(idtnSim) + ( scale(idtnSim) | subID), data=idSim2)
summary(m)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(idtpSim) ~ scale(MemSE) + scale(idtnSim) + (scale(idtnSim) |
## subID)
## Data: idSim2
##
## REML criterion at convergence: 16874.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.4250 -0.6120 -0.2553 0.5019 6.8431
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subID (Intercept) 0.37149 0.6095
## scale(idtnSim) 0.01326 0.1152 0.13
## Residual 0.59743 0.7729
## Number of obs: 6916, groups: subID, 247
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -0.004925 0.040088 239.919466 -0.123 0.902315
## scale(MemSE) 0.045983 0.039932 232.691863 1.152 0.250699
## scale(idtnSim) 0.048829 0.014213 244.906886 3.436 0.000694 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) s(MSE)
## scale(MmSE) -0.001
## scal(dtnSm) 0.068 0.016Negative
m <-lmer(scale(idtnSim) ~ scale(SCC) + scale(idtpSim) + ( scale(idtpSim) | subID), data=idSim2)
summary(m)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(idtnSim) ~ scale(SCC) + scale(idtpSim) + (scale(idtpSim) |
## subID)
## Data: idSim2
##
## REML criterion at convergence: 17114.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.3228 -0.6306 -0.2199 0.4583 3.8839
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subID (Intercept) 0.35107 0.5925
## scale(idtpSim) 0.01435 0.1198 0.08
## Residual 0.62067 0.7878
## Number of obs: 6916, groups: subID, 247
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -0.003114 0.039087 243.531088 -0.080 0.9366
## scale(SCC) -0.045713 0.039033 242.420497 -1.171 0.2427
## scale(idtpSim) 0.057455 0.015168 204.081703 3.788 0.0002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) s(SCC)
## scale(SCC) 0.000
## scal(dtpSm) 0.052 0.004m <-lmer(scale(idtnSim) ~ scale(Ind) + scale(idtpSim) + ( scale(idtpSim) | subID), data=idSim2)
summary(m)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(idtnSim) ~ scale(Ind) + scale(idtpSim) + (scale(idtpSim) |
## subID)
## Data: idSim2
##
## REML criterion at convergence: 17114.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.3224 -0.6300 -0.2198 0.4593 3.8837
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subID (Intercept) 0.35203 0.5933
## scale(idtpSim) 0.01434 0.1198 0.08
## Residual 0.62067 0.7878
## Number of obs: 6916, groups: subID, 247
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -0.003069 0.039137 243.563991 -0.078 0.937567
## scale(Ind) -0.034451 0.039025 240.003313 -0.883 0.378234
## scale(idtpSim) 0.057506 0.015166 203.990240 3.792 0.000197 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) scl(I)
## scale(Ind) -0.001
## scal(dtpSm) 0.052 -0.001m <-lmer(scale(idtnSim) ~ scale(Inter) + scale(idtpSim) + ( scale(idtpSim) | subID), data=idSim2)## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
## Model failed to converge with max|grad| = 0.00245935 (tol = 0.002, component 1)summary(m)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(idtnSim) ~ scale(Inter) + scale(idtpSim) + (scale(idtpSim) |
## subID)
## Data: idSim2
##
## REML criterion at convergence: 17115.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.3224 -0.6303 -0.2207 0.4591 3.8850
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subID (Intercept) 0.35299 0.5941
## scale(idtpSim) 0.01441 0.1201 0.07
## Residual 0.62064 0.7878
## Number of obs: 6916, groups: subID, 247
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -0.003067 0.039188 243.542691 -0.078 0.937690
## scale(Inter) -0.009883 0.039155 242.815304 -0.252 0.800944
## scale(idtpSim) 0.057542 0.015178 203.389925 3.791 0.000198 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) scl(I)
## scale(Intr) 0.002
## scal(dtpSm) 0.050 -0.007
## optimizer (nloptwrap) convergence code: 0 (OK)
## Model failed to converge with max|grad| = 0.00245935 (tol = 0.002, component 1)m <-lmer(scale(idtnSim) ~ scale(SWLS) + scale(idtpSim) + ( scale(idtpSim) | subID), data=idSim2)
summary(m)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(idtnSim) ~ scale(SWLS) + scale(idtpSim) + (scale(idtpSim) |
## subID)
## Data: idSim2
##
## REML criterion at convergence: 17114.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.3230 -0.6316 -0.2207 0.4597 3.8885
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subID (Intercept) 0.35192 0.5932
## scale(idtpSim) 0.01439 0.1200 0.08
## Residual 0.62065 0.7878
## Number of obs: 6916, groups: subID, 247
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -0.003189 0.039131 243.509224 -0.081 0.935113
## scale(SWLS) 0.035244 0.039009 240.387348 0.903 0.367173
## scale(idtpSim) 0.057579 0.015176 203.867312 3.794 0.000195 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) s(SWLS
## scale(SWLS) -0.001
## scal(dtpSm) 0.053 0.007m <-lmer(scale(idtnSim) ~ scale(IdImp) + scale(idtpSim) + ( scale(idtpSim) | subID), data=idSim2)
summary(m)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(idtnSim) ~ scale(IdImp) + scale(idtpSim) + (scale(idtpSim) |
## subID)
## Data: idSim2
##
## REML criterion at convergence: 17115
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.3216 -0.6304 -0.2206 0.4594 3.8855
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subID (Intercept) 0.35250 0.5937
## scale(idtpSim) 0.01436 0.1198 0.07
## Residual 0.62066 0.7878
## Number of obs: 6916, groups: subID, 247
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -0.00296 0.03916 243.61565 -0.076 0.939817
## scale(IdImp) 0.02604 0.03911 242.27078 0.666 0.506150
## scale(idtpSim) 0.05732 0.01517 204.00290 3.778 0.000207 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) sc(II)
## scal(IdImp) 0.002
## scal(dtpSm) 0.049 -0.022m <-lmer(scale(idtnSim) ~ scale(phi) + scale(idtpSim) + ( scale(idtpSim) | subID), data=idSim2)
summary(m)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(idtnSim) ~ scale(phi) + scale(idtpSim) + (scale(idtpSim) |
## subID)
## Data: idSim2
##
## REML criterion at convergence: 17112.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.3187 -0.6322 -0.2210 0.4576 3.8874
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subID (Intercept) 0.34863 0.5904
## scale(idtpSim) 0.01421 0.1192 0.05
## Residual 0.62069 0.7878
## Number of obs: 6916, groups: subID, 247
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -0.002525 0.038960 243.909771 -0.065 0.948373
## scale(phi) -0.068932 0.038923 241.059574 -1.771 0.077828 .
## scale(idtpSim) 0.056774 0.015148 204.734937 3.748 0.000232 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) scl(p)
## scale(phi) -0.004
## scal(dtpSm) 0.041 0.037m <-lmer(scale(idtnSim) ~ scale(overlap_norm) + scale(idtpSim) + ( scale(idtpSim) | subID), data=idSim2)
summary(m)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula:
## scale(idtnSim) ~ scale(overlap_norm) + scale(idtpSim) + (scale(idtpSim) |
## subID)
## Data: idSim2
##
## REML criterion at convergence: 17098.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.3055 -0.6353 -0.2204 0.4564 3.8905
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subID (Intercept) 0.32996 0.5744
## scale(idtpSim) 0.01375 0.1173 0.03
## Residual 0.62072 0.7879
## Number of obs: 6916, groups: subID, 247
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -2.344e-05 3.798e-02 2.467e+02 -0.001 0.99951
## scale(overlap_norm) 1.665e-01 3.950e-02 2.685e+02 4.215 3.42e-05 ***
## scale(idtpSim) 4.812e-02 1.525e-02 2.145e+02 3.156 0.00183 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) scl(_)
## scl(vrlp_n) 0.015
## scal(dtpSm) 0.027 -0.162m <-lmer(scale(idtnSim) ~ scale(H_index) + scale(idtpSim) + ( scale(idtpSim) | subID), data=idSim2)
summary(m)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(idtnSim) ~ scale(H_index) + scale(idtpSim) + (scale(idtpSim) |
## subID)
## Data: idSim2
##
## REML criterion at convergence: 17101
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.3080 -0.6407 -0.2158 0.4612 3.8922
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subID (Intercept) 0.33343 0.5774
## scale(idtpSim) 0.01334 0.1155 0.04
## Residual 0.62094 0.7880
## Number of obs: 6916, groups: subID, 247
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -0.001067 0.038155 245.685802 -0.028 0.977704
## scale(H_index) 0.150428 0.039126 258.420077 3.845 0.000152 ***
## scale(idtpSim) 0.050106 0.015132 207.720234 3.311 0.001095 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) sc(H_)
## scal(H_ndx) 0.010
## scal(dtpSm) 0.034 -0.140m <-lmer(scale(idtnSim) ~ scale(SE) + scale(idtpSim) + ( scale(idtpSim)| subID), data=idSim2)
summary(m)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(idtnSim) ~ scale(SE) + scale(idtpSim) + (scale(idtpSim) |
## subID)
## Data: idSim2
##
## REML criterion at convergence: 17115.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.3222 -0.6301 -0.2206 0.4597 3.8843
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subID (Intercept) 0.35312 0.5942
## scale(idtpSim) 0.01437 0.1199 0.07
## Residual 0.62066 0.7878
## Number of obs: 6916, groups: subID, 247
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -0.003036 0.039194 243.577999 -0.077 0.938315
## scale(SE) -0.003541 0.039110 241.250555 -0.091 0.927924
## scale(idtpSim) 0.057508 0.015170 203.857175 3.791 0.000198 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) sc(SE)
## scale(SE) 0.000
## scal(dtpSm) 0.050 -0.003m <-lmer(scale(idtnSim) ~ scale(NFC) + scale(idtpSim) + ( scale(idtpSim) | subID), data=idSim2)
summary(m)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(idtnSim) ~ scale(NFC) + scale(idtpSim) + (scale(idtpSim) |
## subID)
## Data: idSim2
##
## REML criterion at convergence: 17114.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.3222 -0.6305 -0.2224 0.4599 3.8849
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subID (Intercept) 0.35220 0.5935
## scale(idtpSim) 0.01431 0.1196 0.07
## Residual 0.62068 0.7878
## Number of obs: 6916, groups: subID, 247
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -0.003018 0.039146 243.482429 -0.077 0.938609
## scale(NFC) -0.030124 0.039081 242.089316 -0.771 0.441568
## scale(idtpSim) 0.057569 0.015161 204.014551 3.797 0.000193 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) s(NFC)
## scale(NFC) -0.001
## scal(dtpSm) 0.050 -0.004m <-lmer(scale(idtnSim) ~ scale(DS) + scale(idtpSim) + ( scale(idtpSim) | subID), data=idSim2)
summary(m)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(idtnSim) ~ scale(DS) + scale(idtpSim) + (scale(idtpSim) |
## subID)
## Data: idSim2
##
## REML criterion at convergence: 17115
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.3211 -0.6308 -0.2210 0.4577 3.8843
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subID (Intercept) 0.35260 0.5938
## scale(idtpSim) 0.01426 0.1194 0.07
## Residual 0.62070 0.7878
## Number of obs: 6916, groups: subID, 247
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -0.002947 0.039166 243.605804 -0.075 0.940085
## scale(DS) 0.024557 0.039290 246.031542 0.625 0.532534
## scale(idtpSim) 0.057373 0.015154 203.871968 3.786 0.000201 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) sc(DS)
## scale(DS) 0.003
## scal(dtpSm) 0.049 -0.015m <-lmer(scale(idtnSim) ~ scale(SCC) + scale(idtpSim) + ( scale(idtpSim)| subID), data=idSim2)
summary(m)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(idtnSim) ~ scale(SCC) + scale(idtpSim) + (scale(idtpSim) |
## subID)
## Data: idSim2
##
## REML criterion at convergence: 17114.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.3228 -0.6306 -0.2199 0.4583 3.8839
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subID (Intercept) 0.35107 0.5925
## scale(idtpSim) 0.01435 0.1198 0.08
## Residual 0.62067 0.7878
## Number of obs: 6916, groups: subID, 247
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -0.003114 0.039087 243.531088 -0.080 0.9366
## scale(SCC) -0.045713 0.039033 242.420497 -1.171 0.2427
## scale(idtpSim) 0.057455 0.015168 204.081703 3.788 0.0002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) s(SCC)
## scale(SCC) 0.000
## scal(dtpSm) 0.052 0.004m <-lmer(scale(idtnSim) ~ scale(MemSE) + scale(idtpSim) + ( scale(idtpSim) | subID), data=idSim2)
summary(m)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(idtnSim) ~ scale(MemSE) + scale(idtpSim) + (scale(idtpSim) |
## subID)
## Data: idSim2
##
## REML criterion at convergence: 17112.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.3237 -0.6290 -0.2199 0.4586 3.8899
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subID (Intercept) 0.34902 0.5908
## scale(idtpSim) 0.01443 0.1201 0.07
## Residual 0.62065 0.7878
## Number of obs: 6916, groups: subID, 247
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -0.003076 0.038982 243.316102 -0.079 0.937172
## scale(MemSE) -0.061996 0.038966 242.802001 -1.591 0.112902
## scale(idtpSim) 0.057976 0.015179 204.272822 3.820 0.000177 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) s(MSE)
## scale(MmSE) 0.001
## scal(dtpSm) 0.049 -0.013m <-lmer(scale(idtnSim) ~ scale(PrivCSE) + scale(idtpSim) + ( scale(idtpSim) | subID), data=idSim2)
summary(m)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(idtnSim) ~ scale(PrivCSE) + scale(idtpSim) + (scale(idtpSim) |
## subID)
## Data: idSim2
##
## REML criterion at convergence: 17110.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.3256 -0.6310 -0.2192 0.4593 3.8941
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subID (Intercept) 0.34478 0.5872
## scale(idtpSim) 0.01442 0.1201 0.08
## Residual 0.62067 0.7878
## Number of obs: 6916, groups: subID, 247
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -0.003396 0.038760 243.061976 -0.088 0.930245
## scale(PrivCSE) -0.088932 0.038752 242.932615 -2.295 0.022592 *
## scale(idtpSim) 0.058256 0.015178 204.368511 3.838 0.000165 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) s(PCSE
## scl(PrvCSE) 0.002
## scal(dtpSm) 0.055 -0.016m <-lmer(scale(idtnSim) ~ scale(PubCSE) + scale(idtpSim) + ( scale(idtpSim) | subID), data=idSim2)
summary(m)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(idtnSim) ~ scale(PubCSE) + scale(idtpSim) + (scale(idtpSim) |
## subID)
## Data: idSim2
##
## REML criterion at convergence: 17115.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.3223 -0.6293 -0.2202 0.4598 3.8842
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subID (Intercept) 0.35286 0.5940
## scale(idtpSim) 0.01435 0.1198 0.08
## Residual 0.62067 0.7878
## Number of obs: 6916, groups: subID, 247
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -0.003085 0.039180 243.541210 -0.079 0.937296
## scale(PubCSE) -0.017932 0.039096 241.286396 -0.459 0.646878
## scale(idtpSim) 0.057515 0.015169 203.930805 3.792 0.000197 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) s(PCSE
## scal(PbCSE) -0.001
## scal(dtpSm) 0.052 -0.006m <-lmer(scale(idtnSim) ~ scale(MemSE) + scale(idtpSim) + ( scale(idtpSim) | subID), data=idSim2)
summary(m)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(idtnSim) ~ scale(MemSE) + scale(idtpSim) + (scale(idtpSim) |
## subID)
## Data: idSim2
##
## REML criterion at convergence: 17112.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.3237 -0.6290 -0.2199 0.4586 3.8899
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subID (Intercept) 0.34902 0.5908
## scale(idtpSim) 0.01443 0.1201 0.07
## Residual 0.62065 0.7878
## Number of obs: 6916, groups: subID, 247
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -0.003076 0.038982 243.316102 -0.079 0.937172
## scale(MemSE) -0.061996 0.038966 242.802001 -1.591 0.112902
## scale(idtpSim) 0.057976 0.015179 204.272822 3.820 0.000177 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) s(MSE)
## scale(MmSE) 0.001
## scal(dtpSm) 0.049 -0.013Both
m <-lmer(scale(idtSim) ~ scale(SCC) + ( 1 | subID), data=idSim2)
summary(m)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(idtSim) ~ scale(SCC) + (1 | subID)
## Data: idSim2
##
## REML criterion at convergence: 16838.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.6165 -0.6416 -0.2314 0.5058 7.9550
##
## Random effects:
## Groups Name Variance Std.Dev.
## subID (Intercept) 0.4033 0.6350
## Residual 0.6000 0.7746
## Number of obs: 6916, groups: subID, 247
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 1.547e-14 4.147e-02 2.450e+02 0.000 1.000
## scale(SCC) -9.042e-03 4.147e-02 2.450e+02 -0.218 0.828
##
## Correlation of Fixed Effects:
## (Intr)
## scale(SCC) 0.000m <-lmer(scale(idtSim) ~ scale(Ind) + ( 1 | subID), data=idSim2)
summary(m)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(idtSim) ~ scale(Ind) + (1 | subID)
## Data: idSim2
##
## REML criterion at convergence: 16838.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.6164 -0.6406 -0.2313 0.5061 7.9555
##
## Random effects:
## Groups Name Variance Std.Dev.
## subID (Intercept) 0.4032 0.6350
## Residual 0.6000 0.7746
## Number of obs: 6916, groups: subID, 247
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 1.389e-14 4.146e-02 2.450e+02 0.000 1.000
## scale(Ind) 1.146e-02 4.147e-02 2.450e+02 0.276 0.783
##
## Correlation of Fixed Effects:
## (Intr)
## scale(Ind) 0.000m <-lmer(scale(idtSim) ~ scale(Inter) + ( 1 | subID), data=idSim2)
summary(m)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(idtSim) ~ scale(Inter) + (1 | subID)
## Data: idSim2
##
## REML criterion at convergence: 16837.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.6166 -0.6403 -0.2326 0.5069 7.9548
##
## Random effects:
## Groups Name Variance Std.Dev.
## subID (Intercept) 0.4015 0.6336
## Residual 0.6000 0.7746
## Number of obs: 6916, groups: subID, 247
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 1.461e-14 4.138e-02 2.450e+02 0.000 1.000
## scale(Inter) 4.306e-02 4.138e-02 2.450e+02 1.041 0.299
##
## Correlation of Fixed Effects:
## (Intr)
## scale(Intr) 0.000m <-lmer(scale(idtSim) ~ scale(SWLS) + ( 1 | subID), data=idSim2)
summary(m)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(idtSim) ~ scale(SWLS) + (1 | subID)
## Data: idSim2
##
## REML criterion at convergence: 16837.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.6150 -0.6402 -0.2320 0.5064 7.9506
##
## Random effects:
## Groups Name Variance Std.Dev.
## subID (Intercept) 0.4027 0.6346
## Residual 0.6000 0.7746
## Number of obs: 6916, groups: subID, 247
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 1.355e-14 4.144e-02 2.450e+02 0.000 1.000
## scale(SWLS) -2.505e-02 4.144e-02 2.450e+02 -0.604 0.546
##
## Correlation of Fixed Effects:
## (Intr)
## scale(SWLS) 0.000m <-lmer(scale(idtSim) ~ scale(IdImp) + ( 1 | subID), data=idSim2)
summary(m)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(idtSim) ~ scale(IdImp) + (1 | subID)
## Data: idSim2
##
## REML criterion at convergence: 16835.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.6158 -0.6404 -0.2316 0.5046 7.9560
##
## Random effects:
## Groups Name Variance Std.Dev.
## subID (Intercept) 0.3987 0.6314
## Residual 0.6000 0.7746
## Number of obs: 6916, groups: subID, 247
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 1.467e-14 4.124e-02 2.450e+02 0.000 1.000
## scale(IdImp) 6.760e-02 4.125e-02 2.450e+02 1.639 0.103
##
## Correlation of Fixed Effects:
## (Intr)
## scal(IdImp) 0.000m <-lmer(scale(idtSim) ~ scale(phi) + ( 1 | subID), data=idSim2)
summary(m)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(idtSim) ~ scale(phi) + (1 | subID)
## Data: idSim2
##
## REML criterion at convergence: 16833.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.6193 -0.6376 -0.2324 0.5064 7.9523
##
## Random effects:
## Groups Name Variance Std.Dev.
## subID (Intercept) 0.3953 0.6288
## Residual 0.6000 0.7746
## Number of obs: 6916, groups: subID, 247
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 1.426e-14 4.108e-02 2.450e+02 0.000 1.0000
## scale(phi) -8.908e-02 4.108e-02 2.450e+02 -2.168 0.0311 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## scale(phi) 0.000m <-lmer(scale(idtSim) ~ scale(overlap_norm) + ( 1 | subID), data=idSim2)
summary(m)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(idtSim) ~ scale(overlap_norm) + (1 | subID)
## Data: idSim2
##
## REML criterion at convergence: 16214.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.5350 -0.6414 -0.1935 0.5300 8.0647
##
## Random effects:
## Groups Name Variance Std.Dev.
## subID (Intercept) 0.01193 0.1092
## Residual 0.59996 0.7746
## Number of obs: 6916, groups: subID, 247
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 1.917e-15 1.162e-02 2.450e+02 0.00 1
## scale(overlap_norm) 6.231e-01 1.162e-02 2.450e+02 53.61 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## scl(vrlp_n) 0.000m <-lmer(scale(idtSim) ~ scale(H_index) + ( 1 | subID), data=idSim2)
summary(m)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(idtSim) ~ scale(H_index) + (1 | subID)
## Data: idSim2
##
## REML criterion at convergence: 16638.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.3419 -0.6418 -0.2390 0.4970 8.0199
##
## Random effects:
## Groups Name Variance Std.Dev.
## subID (Intercept) 0.1665 0.4080
## Residual 0.6000 0.7746
## Number of obs: 6916, groups: subID, 247
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 6.823e-15 2.758e-02 2.450e+02 0.00 1
## scale(H_index) 4.848e-01 2.758e-02 2.450e+02 17.57 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## scal(H_ndx) 0.000m <-lmer(scale(idtSim) ~ scale(SE) + ( 1| subID), data=idSim2)
summary(m)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(idtSim) ~ scale(SE) + (1 | subID)
## Data: idSim2
##
## REML criterion at convergence: 16837.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.6177 -0.6415 -0.2313 0.5060 7.9568
##
## Random effects:
## Groups Name Variance Std.Dev.
## subID (Intercept) 0.4027 0.6346
## Residual 0.6000 0.7746
## Number of obs: 6916, groups: subID, 247
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 1.342e-14 4.144e-02 2.450e+02 0.000 1.000
## scale(SE) 2.515e-02 4.144e-02 2.450e+02 0.607 0.545
##
## Correlation of Fixed Effects:
## (Intr)
## scale(SE) 0.000m <-lmer(scale(idtSim) ~ scale(NFC) + ( 1 | subID), data=idSim2)
summary(m)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(idtSim) ~ scale(NFC) + (1 | subID)
## Data: idSim2
##
## REML criterion at convergence: 16837.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.6158 -0.6429 -0.2305 0.5048 7.9553
##
## Random effects:
## Groups Name Variance Std.Dev.
## subID (Intercept) 0.4027 0.6346
## Residual 0.6000 0.7746
## Number of obs: 6916, groups: subID, 247
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 1.463e-14 4.144e-02 2.450e+02 0.000 1.000
## scale(NFC) 2.427e-02 4.144e-02 2.450e+02 0.586 0.559
##
## Correlation of Fixed Effects:
## (Intr)
## scale(NFC) 0.000m <-lmer(scale(idtSim) ~ scale(DS) + ( 1 | subID), data=idSim2)
summary(m)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(idtSim) ~ scale(DS) + (1 | subID)
## Data: idSim2
##
## REML criterion at convergence: 16835.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.6145 -0.6408 -0.2325 0.5041 7.9510
##
## Random effects:
## Groups Name Variance Std.Dev.
## subID (Intercept) 0.3991 0.6318
## Residual 0.6000 0.7746
## Number of obs: 6916, groups: subID, 247
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 1.239e-14 4.126e-02 2.450e+02 0.000 1.000
## scale(DS) 6.470e-02 4.127e-02 2.450e+02 1.568 0.118
##
## Correlation of Fixed Effects:
## (Intr)
## scale(DS) 0.000m <-lmer(scale(idtSim) ~ scale(SCC) + ( 1| subID), data=idSim2)
summary(m)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(idtSim) ~ scale(SCC) + (1 | subID)
## Data: idSim2
##
## REML criterion at convergence: 16838.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.6165 -0.6416 -0.2314 0.5058 7.9550
##
## Random effects:
## Groups Name Variance Std.Dev.
## subID (Intercept) 0.4033 0.6350
## Residual 0.6000 0.7746
## Number of obs: 6916, groups: subID, 247
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 1.547e-14 4.147e-02 2.450e+02 0.000 1.000
## scale(SCC) -9.042e-03 4.147e-02 2.450e+02 -0.218 0.828
##
## Correlation of Fixed Effects:
## (Intr)
## scale(SCC) 0.000m <-lmer(scale(idtSim) ~ scale(MemSE) + ( 1 | subID), data=idSim2)
summary(m)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(idtSim) ~ scale(MemSE) + (1 | subID)
## Data: idSim2
##
## REML criterion at convergence: 16837.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.6135 -0.6411 -0.2318 0.5057 7.9567
##
## Random effects:
## Groups Name Variance Std.Dev.
## subID (Intercept) 0.4019 0.6340
## Residual 0.6000 0.7746
## Number of obs: 6916, groups: subID, 247
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 1.276e-14 4.140e-02 2.450e+02 0.000 1.000
## scale(MemSE) 3.735e-02 4.140e-02 2.450e+02 0.902 0.368
##
## Correlation of Fixed Effects:
## (Intr)
## scale(MmSE) 0.000m <-lmer(scale(idtSim) ~ scale(PrivCSE) + ( 1 | subID), data=idSim2)
summary(m)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(idtSim) ~ scale(PrivCSE) + (1 | subID)
## Data: idSim2
##
## REML criterion at convergence: 16835.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.6138 -0.6364 -0.2310 0.5063 7.9593
##
## Random effects:
## Groups Name Variance Std.Dev.
## subID (Intercept) 0.3982 0.6310
## Residual 0.6000 0.7746
## Number of obs: 6916, groups: subID, 247
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 1.411e-14 4.122e-02 2.450e+02 0.000 1.0000
## scale(PrivCSE) 7.145e-02 4.122e-02 2.450e+02 1.733 0.0843 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## scl(PrvCSE) 0.000m <-lmer(scale(idtSim) ~ scale(PubCSE) + ( 1 | subID), data=idSim2)
summary(m)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(idtSim) ~ scale(PubCSE) + (1 | subID)
## Data: idSim2
##
## REML criterion at convergence: 16838
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.6158 -0.6420 -0.2325 0.5049 7.9558
##
## Random effects:
## Groups Name Variance Std.Dev.
## subID (Intercept) 0.4029 0.6347
## Residual 0.6000 0.7746
## Number of obs: 6916, groups: subID, 247
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 1.250e-14 4.145e-02 2.450e+02 0.000 1.000
## scale(PubCSE) 2.142e-02 4.145e-02 2.450e+02 0.517 0.606
##
## Correlation of Fixed Effects:
## (Intr)
## scal(PbCSE) 0.000m <-lmer(scale(idtSim) ~ scale(MemSE) + ( 1 | subID), data=idSim2)
summary(m)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(idtSim) ~ scale(MemSE) + (1 | subID)
## Data: idSim2
##
## REML criterion at convergence: 16837.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.6135 -0.6411 -0.2318 0.5057 7.9567
##
## Random effects:
## Groups Name Variance Std.Dev.
## subID (Intercept) 0.4019 0.6340
## Residual 0.6000 0.7746
## Number of obs: 6916, groups: subID, 247
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 1.276e-14 4.140e-02 2.450e+02 0.000 1.000
## scale(MemSE) 3.735e-02 4.140e-02 2.450e+02 0.902 0.368
##
## Correlation of Fixed Effects:
## (Intr)
## scale(MmSE) 0.000Individual Differences Predict Pairwise Overlap in Identities in Common
m <-lmer(scale(idSim) ~ scale(SCC) + ( 1 | subID), data=idSim2)
summary(m)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(idSim) ~ scale(SCC) + (1 | subID)
## Data: idSim2
##
## REML criterion at convergence: 17147.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.3152 -0.6196 -0.2368 0.4783 3.8478
##
## Random effects:
## Groups Name Variance Std.Dev.
## subID (Intercept) 0.3708 0.6089
## Residual 0.6302 0.7939
## Number of obs: 6916, groups: subID, 247
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -2.516e-14 3.990e-02 2.450e+02 0.000 1.000
## scale(SCC) -4.540e-02 3.990e-02 2.450e+02 -1.138 0.256
##
## Correlation of Fixed Effects:
## (Intr)
## scale(SCC) 0.000m <-lmer(scale(idSim) ~ scale(Ind) + ( 1 | subID), data=idSim2)
summary(m)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(idSim) ~ scale(Ind) + (1 | subID)
## Data: idSim2
##
## REML criterion at convergence: 17148.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.3144 -0.6212 -0.2375 0.4812 3.8470
##
## Random effects:
## Groups Name Variance Std.Dev.
## subID (Intercept) 0.3716 0.6096
## Residual 0.6302 0.7939
## Number of obs: 6916, groups: subID, 247
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -2.510e-14 3.995e-02 2.450e+02 0.000 1.000
## scale(Ind) -3.467e-02 3.995e-02 2.450e+02 -0.868 0.386
##
## Correlation of Fixed Effects:
## (Intr)
## scale(Ind) 0.000m <-lmer(scale(idSim) ~ scale(Inter) + ( 1 | subID), data=idSim2)
summary(m)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(idSim) ~ scale(Inter) + (1 | subID)
## Data: idSim2
##
## REML criterion at convergence: 17148.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.3165 -0.6252 -0.2399 0.4817 3.8424
##
## Random effects:
## Groups Name Variance Std.Dev.
## subID (Intercept) 0.3728 0.6106
## Residual 0.6302 0.7939
## Number of obs: 6916, groups: subID, 247
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -2.750e-14 4.001e-02 2.450e+02 0.000 1.000
## scale(Inter) -9.072e-04 4.001e-02 2.450e+02 -0.023 0.982
##
## Correlation of Fixed Effects:
## (Intr)
## scale(Intr) 0.000m <-lmer(scale(idSim) ~ scale(SWLS) + ( 1 | subID), data=idSim2)
summary(m)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(idSim) ~ scale(SWLS) + (1 | subID)
## Data: idSim2
##
## REML criterion at convergence: 17148.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.3162 -0.6289 -0.2364 0.4807 3.8476
##
## Random effects:
## Groups Name Variance Std.Dev.
## subID (Intercept) 0.3719 0.6098
## Residual 0.6302 0.7939
## Number of obs: 6916, groups: subID, 247
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -2.929e-14 3.996e-02 2.450e+02 0.000 1.000
## scale(SWLS) 3.016e-02 3.996e-02 2.450e+02 0.755 0.451
##
## Correlation of Fixed Effects:
## (Intr)
## scale(SWLS) 0.000m <-lmer(scale(idSim) ~ scale(IdImp) + ( 1 | subID), data=idSim2)
summary(m)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(idSim) ~ scale(IdImp) + (1 | subID)
## Data: idSim2
##
## REML criterion at convergence: 17148.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.3145 -0.6296 -0.2388 0.4832 3.8486
##
## Random effects:
## Groups Name Variance Std.Dev.
## subID (Intercept) 0.3718 0.6098
## Residual 0.6302 0.7939
## Number of obs: 6916, groups: subID, 247
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -2.759e-14 3.996e-02 2.450e+02 0.000 1.000
## scale(IdImp) 3.180e-02 3.996e-02 2.450e+02 0.796 0.427
##
## Correlation of Fixed Effects:
## (Intr)
## scal(IdImp) 0.000m <-lmer(scale(idSim) ~ scale(phi) + ( 1 | subID), data=idSim2)
summary(m)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(idSim) ~ scale(phi) + (1 | subID)
## Data: idSim2
##
## REML criterion at convergence: 17144.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.3162 -0.6263 -0.2362 0.4828 3.8520
##
## Random effects:
## Groups Name Variance Std.Dev.
## subID (Intercept) 0.3662 0.6052
## Residual 0.6302 0.7939
## Number of obs: 6916, groups: subID, 247
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -2.715e-14 3.967e-02 2.450e+02 0.000 1.0000
## scale(phi) -8.104e-02 3.967e-02 2.450e+02 -2.043 0.0422 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## scale(phi) 0.000m <-lmer(scale(idSim) ~ scale(overlap_norm) + ( 1 | subID), data=idSim2)
summary(m)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(idSim) ~ scale(overlap_norm) + (1 | subID)
## Data: idSim2
##
## REML criterion at convergence: 17124.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.3312 -0.6329 -0.2323 0.4839 3.8549
##
## Random effects:
## Groups Name Variance Std.Dev.
## subID (Intercept) 0.3350 0.5788
## Residual 0.6302 0.7939
## Number of obs: 6916, groups: subID, 247
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -2.017e-14 3.804e-02 2.450e+02 0.000 1
## scale(overlap_norm) 1.937e-01 3.805e-02 2.450e+02 5.092 7.08e-07 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## scl(vrlp_n) 0.000m <-lmer(scale(idSim) ~ scale(H_index) + ( 1 | subID), data=idSim2)
summary(m)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(idSim) ~ scale(H_index) + (1 | subID)
## Data: idSim2
##
## REML criterion at convergence: 17126.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.3360 -0.6351 -0.2269 0.4837 3.8608
##
## Random effects:
## Groups Name Variance Std.Dev.
## subID (Intercept) 0.3390 0.5822
## Residual 0.6302 0.7939
## Number of obs: 6916, groups: subID, 247
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -2.558e-14 3.825e-02 2.450e+02 0.000 1
## scale(H_index) 1.833e-01 3.826e-02 2.450e+02 4.792 2.86e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## scal(H_ndx) 0.000m <-lmer(scale(idSim) ~ scale(SE) + ( 1| subID), data=idSim2)
summary(m)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(idSim) ~ scale(SE) + (1 | subID)
## Data: idSim2
##
## REML criterion at convergence: 17148.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.3164 -0.6246 -0.2397 0.4815 3.8430
##
## Random effects:
## Groups Name Variance Std.Dev.
## subID (Intercept) 0.3728 0.6106
## Residual 0.6302 0.7939
## Number of obs: 6916, groups: subID, 247
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -2.477e-14 4.001e-02 2.450e+02 0.000 1.000
## scale(SE) -6.099e-03 4.001e-02 2.450e+02 -0.152 0.879
##
## Correlation of Fixed Effects:
## (Intr)
## scale(SE) 0.000m <-lmer(scale(idSim) ~ scale(NFC) + ( 1 | subID), data=idSim2)
summary(m)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(idSim) ~ scale(NFC) + (1 | subID)
## Data: idSim2
##
## REML criterion at convergence: 17148.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.3184 -0.6284 -0.2377 0.4783 3.8474
##
## Random effects:
## Groups Name Variance Std.Dev.
## subID (Intercept) 0.3718 0.6097
## Residual 0.6302 0.7939
## Number of obs: 6916, groups: subID, 247
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -2.550e-14 3.995e-02 2.450e+02 0.000 1.000
## scale(NFC) -3.243e-02 3.996e-02 2.450e+02 -0.812 0.418
##
## Correlation of Fixed Effects:
## (Intr)
## scale(NFC) 0.000m <-lmer(scale(idSim) ~ scale(DS) + ( 1 | subID), data=idSim2)
summary(m)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(idSim) ~ scale(DS) + (1 | subID)
## Data: idSim2
##
## REML criterion at convergence: 17148.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.3166 -0.6176 -0.2346 0.4801 3.8470
##
## Random effects:
## Groups Name Variance Std.Dev.
## subID (Intercept) 0.3716 0.6096
## Residual 0.6302 0.7939
## Number of obs: 6916, groups: subID, 247
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -2.578e-14 3.994e-02 2.450e+02 0.000 1.000
## scale(DS) 3.498e-02 3.995e-02 2.450e+02 0.876 0.382
##
## Correlation of Fixed Effects:
## (Intr)
## scale(DS) 0.000m <-lmer(scale(idSim) ~ scale(SCC) + ( 1| subID), data=idSim2)
summary(m)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(idSim) ~ scale(SCC) + (1 | subID)
## Data: idSim2
##
## REML criterion at convergence: 17147.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.3152 -0.6196 -0.2368 0.4783 3.8478
##
## Random effects:
## Groups Name Variance Std.Dev.
## subID (Intercept) 0.3708 0.6089
## Residual 0.6302 0.7939
## Number of obs: 6916, groups: subID, 247
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -2.516e-14 3.990e-02 2.450e+02 0.000 1.000
## scale(SCC) -4.540e-02 3.990e-02 2.450e+02 -1.138 0.256
##
## Correlation of Fixed Effects:
## (Intr)
## scale(SCC) 0.000m <-lmer(scale(idSim) ~ scale(MemSE) + ( 1 | subID), data=idSim2)
summary(m)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(idSim) ~ scale(MemSE) + (1 | subID)
## Data: idSim2
##
## REML criterion at convergence: 17146.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.3150 -0.6168 -0.2350 0.4811 3.8506
##
## Random effects:
## Groups Name Variance Std.Dev.
## subID (Intercept) 0.3695 0.6078
## Residual 0.6302 0.7939
## Number of obs: 6916, groups: subID, 247
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -2.725e-14 3.984e-02 2.450e+02 0.000 1.000
## scale(MemSE) -5.771e-02 3.984e-02 2.450e+02 -1.448 0.149
##
## Correlation of Fixed Effects:
## (Intr)
## scale(MmSE) 0.000m <-lmer(scale(idSim) ~ scale(PrivCSE) + ( 1 | subID), data=idSim2)
summary(m)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(idSim) ~ scale(PrivCSE) + (1 | subID)
## Data: idSim2
##
## REML criterion at convergence: 17144.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.3200 -0.6124 -0.2308 0.4798 3.8547
##
## Random effects:
## Groups Name Variance Std.Dev.
## subID (Intercept) 0.3658 0.6048
## Residual 0.6302 0.7939
## Number of obs: 6916, groups: subID, 247
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -2.822e-14 3.965e-02 2.450e+02 0.000 1.0000
## scale(PrivCSE) -8.376e-02 3.965e-02 2.450e+02 -2.113 0.0357 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## scl(PrvCSE) 0.000m <-lmer(scale(idSim) ~ scale(PubCSE) + ( 1 | subID), data=idSim2)
summary(m)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(idSim) ~ scale(PubCSE) + (1 | subID)
## Data: idSim2
##
## REML criterion at convergence: 17148.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.3164 -0.6223 -0.2388 0.4827 3.8453
##
## Random effects:
## Groups Name Variance Std.Dev.
## subID (Intercept) 0.3726 0.6104
## Residual 0.6302 0.7939
## Number of obs: 6916, groups: subID, 247
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -2.597e-14 3.999e-02 2.450e+02 0.000 1.000
## scale(PubCSE) -1.659e-02 4.000e-02 2.450e+02 -0.415 0.679
##
## Correlation of Fixed Effects:
## (Intr)
## scal(PbCSE) 0.000m <-lmer(scale(idSim) ~ scale(MemSE) + ( 1 | subID), data=idSim2)
summary(m)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(idSim) ~ scale(MemSE) + (1 | subID)
## Data: idSim2
##
## REML criterion at convergence: 17146.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.3150 -0.6168 -0.2350 0.4811 3.8506
##
## Random effects:
## Groups Name Variance Std.Dev.
## subID (Intercept) 0.3695 0.6078
## Residual 0.6302 0.7939
## Number of obs: 6916, groups: subID, 247
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -2.725e-14 3.984e-02 2.450e+02 0.000 1.000
## scale(MemSE) -5.771e-02 3.984e-02 2.450e+02 -1.448 0.149
##
## Correlation of Fixed Effects:
## (Intr)
## scale(MmSE) 0.000More trait overlap predicts more intergroup bias
Study 2 Only
m<-lmer(scale(interG) ~ scale(traitCommNod) + scale(idCommNod) + ( scale(traitCommNod) + scale(idCommNod) | subID) + ( 1 | id), data=idShort2)## boundary (singular) fit: see help('isSingular')summary(m)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula:
## scale(interG) ~ scale(traitCommNod) + scale(idCommNod) + (scale(traitCommNod) +
## scale(idCommNod) | subID) + (1 | id)
## Data: idShort2
##
## REML criterion at convergence: 3879.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.0433 -0.5218 -0.1128 0.4632 3.8308
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subID (Intercept) 0.27073 0.52032
## scale(traitCommNod) 0.00535 0.07315 1.00
## scale(idCommNod) 0.01093 0.10454 -1.00 -1.00
## id (Intercept) 0.03795 0.19482
## Residual 0.68072 0.82506
## Number of obs: 1456, groups: subID, 229; id, 8
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -0.01010 0.08042 10.68542 -0.126 0.9024
## scale(traitCommNod) 0.10010 0.04370 189.51160 2.291 0.0231 *
## scale(idCommNod) -0.07787 0.04035 564.23880 -1.930 0.0541 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) scl(tCN)
## scl(trtCmN) 0.066
## scl(dCmmNd) -0.117 -0.581
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')Identity Centrality Predicts Less Mutability
Study 1 Only
mall.I2I<-lmer(scale(mall) ~ scale(I2Ideg) + ( scale(I2Ideg) | subID) + ( 1 | id), data=idShort1)## boundary (singular) fit: see help('isSingular')summary(mall.I2I)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(mall) ~ scale(I2Ideg) + (scale(I2Ideg) | subID) + (1 | id)
## Data: idShort1
##
## REML criterion at convergence: 5055.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.2762 -0.6723 0.0482 0.6931 2.5704
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subID (Intercept) 0.136687 0.36971
## scale(I2Ideg) 0.002015 0.04489 1.00
## id (Intercept) 0.235380 0.48516
## Residual 0.662695 0.81406
## Number of obs: 1968, groups: subID, 246; id, 8
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 1.002e-03 1.741e-01 7.259e+00 0.006 0.9956
## scale(I2Ideg) 5.031e-02 2.738e-02 1.930e+02 1.837 0.0677 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## scal(I2Idg) 0.022
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')Positivity of Identity Associated with Greater Intergroup Bias
Study 2 Only
More positive self-reported
m<-lmer(scale(interG) ~ scale(pos) + ( scale(pos) | subID) + ( 1 | id), data=idShort2)
summary(m)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(interG) ~ scale(pos) + (scale(pos) | subID) + (1 | id)
## Data: idShort2
##
## REML criterion at convergence: 3755.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.0986 -0.5334 -0.1214 0.4823 3.5762
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subID (Intercept) 0.27706 0.5264
## scale(pos) 0.04348 0.2085 0.34
## id (Intercept) 0.06469 0.2543
## Residual 0.58541 0.7651
## Number of obs: 1456, groups: subID, 229; id, 8
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -0.01950 0.09897 9.15112 -0.197 0.848
## scale(pos) 0.26915 0.02943 217.33810 9.147 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## scale(pos) 0.045More positive traits associated with identity than negative
m<-lmer(scale(interG) ~ scale(pndiff) + ( scale(pndiff) | subID) + ( 1 | id), data=idShort2)
summary(m)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(interG) ~ scale(pndiff) + (scale(pndiff) | subID) + (1 |
## id)
## Data: idShort2
##
## REML criterion at convergence: 3858.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.6740 -0.5202 -0.1112 0.4929 3.8639
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subID (Intercept) 0.27711 0.5264
## scale(pndiff) 0.04091 0.2023 0.26
## id (Intercept) 0.03837 0.1959
## Residual 0.64979 0.8061
## Number of obs: 1456, groups: subID, 229; id, 8
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 0.008655 0.080989 10.832624 0.107 0.917
## scale(pndiff) 0.195757 0.036648 66.891671 5.342 1.19e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## scal(pndff) 0.075Differentiation and Inclusion Quadratic Association
Study 2 Only
m<-lmer(scale(differ) ~ scale(poly(inclus, 2)) + ( scale(inclus) | subID) + ( 1 | id), data=idShort2[!is.na(idShort2$differ) & !is.na(idShort2$inclus), ])
summary(m)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(differ) ~ scale(poly(inclus, 2)) + (scale(inclus) | subID) +
## (1 | id)
## Data: idShort2[!is.na(idShort2$differ) & !is.na(idShort2$inclus), ]
##
## REML criterion at convergence: 5090.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.7915 -0.5418 -0.0094 0.6313 3.2085
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subID (Intercept) 0.21794 0.4668
## scale(inclus) 0.05309 0.2304 0.62
## id (Intercept) 0.02964 0.1722
## Residual 0.62675 0.7917
## Number of obs: 1965, groups: subID, 247; id, 8
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -0.004591 0.070413 10.429788 -0.065 0.949
## scale(poly(inclus, 2))1 0.242719 0.026989 258.096574 8.993 < 2e-16 ***
## scale(poly(inclus, 2))2 0.091903 0.021812 755.472716 4.213 2.82e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) s((,2))1
## scl(p(,2))1 0.150
## scl(p(,2))2 0.033 0.077ggpredict(m, c( "inclus" )) %>% plot(show.title=FALSE)## Model contains splines or polynomial terms. Consider using `terms="inclus [all]"` to get smooth plots. See also package-vignette 'Marginal Effects at Specific Values'.
# Differentiation and Inclusion Interact in Predicting
Identification
Study 2 Only
m<-lmer(scale(streng) ~ scale(inclus) * scale(differ) + ( scale(inclus) + scale(differ) | subID) + ( 1 | id), data=idShort2)## boundary (singular) fit: see help('isSingular')summary(m)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(streng) ~ scale(inclus) * scale(differ) + (scale(inclus) +
## scale(differ) | subID) + (1 | id)
## Data: idShort2
##
## REML criterion at convergence: 4361.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -7.7156 -0.2872 0.0740 0.5601 3.3661
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subID (Intercept) 0.087236 0.29536
## scale(inclus) 0.069104 0.26288 -1.00
## scale(differ) 0.001232 0.03511 -1.00 1.00
## id (Intercept) 0.092800 0.30463
## Residual 0.453181 0.67319
## Number of obs: 1965, groups: subID, 247; id, 8
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 0.03494 0.11066 7.44799 0.316 0.760879
## scale(inclus) 0.39340 0.02612 321.79456 15.058 < 2e-16
## scale(differ) 0.09759 0.01838 1371.69049 5.308 1.29e-07
## scale(inclus):scale(differ) -0.06436 0.01770 1776.81985 -3.637 0.000284
##
## (Intercept)
## scale(inclus) ***
## scale(differ) ***
## scale(inclus):scale(differ) ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) scl(n) scl(d)
## scale(ncls) -0.134
## scale(dffr) -0.013 -0.085
## scl(ncl):() -0.026 0.013 -0.357
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')ggpredict(m, c( "inclus" , "differ")) %>% plot(show.title=FALSE)
m<-lmer(scale(streng) ~ scale(poly(inclus, 2)) + scale(poly(differ, 2)) + ( scale(poly(inclus, 2)) + scale(poly(differ, 2)) | subID) + ( 1 | id), data=idShort2[!is.na(idShort2$differ) & !is.na(idShort2$inclus), ])## boundary (singular) fit: see help('isSingular')summary(m)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(streng) ~ scale(poly(inclus, 2)) + scale(poly(differ, 2)) +
## (scale(poly(inclus, 2)) + scale(poly(differ, 2)) | subID) + (1 | id)
## Data: idShort2[!is.na(idShort2$differ) & !is.na(idShort2$inclus), ]
##
## REML criterion at convergence: 4352.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -7.8400 -0.3756 0.0737 0.5286 3.4453
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subID (Intercept) 0.093505 0.30579
## scale(poly(inclus, 2))1 0.069524 0.26367 -0.89
## scale(poly(inclus, 2))2 0.018036 0.13430 -0.15 -0.32
## scale(poly(differ, 2))1 0.001403 0.03745 -1.00 0.93 0.06
## scale(poly(differ, 2))2 0.000141 0.01188 0.87 -0.55 -0.62 -0.82
## id (Intercept) 0.093977 0.30656
## Residual 0.435159 0.65967
## Number of obs: 1965, groups: subID, 247; id, 8
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 0.01446 0.11144 7.47900 0.130 0.90021
## scale(poly(inclus, 2))1 0.39923 0.02785 214.68616 14.336 < 2e-16 ***
## scale(poly(inclus, 2))2 -0.03643 0.02304 165.16520 -1.581 0.11578
## scale(poly(differ, 2))1 0.07732 0.01751 548.65839 4.417 1.21e-05 ***
## scale(poly(differ, 2))2 0.04949 0.01716 244.45174 2.885 0.00427 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) scl(ply(n,2))1 scl(ply(n,2))2 scl(ply(d,2))1
## scl(ply(n,2))1 -0.129
## scl(ply(n,2))2 0.015 -0.298
## scl(ply(d,2))1 -0.026 -0.088 -0.052
## scl(ply(d,2))2 0.007 -0.135 -0.110 -0.003
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')ggpredict(m, c( "inclus[all]" , "differ[all]")) %>% plot(show.title=FALSE)
Network Measures Correlated with External Measures
attempted in tidyverse
# networkMeasures %>% map(~ .x) %>% select(indDiff1, SE:CESD, .)
#
# selected<-map(networkMeasures, ~ select(indDiff1, SE:CESD, .) )
# map2(networkMeasures, ~ selected, networkMeasures )Study 1
networkMeasures <- indDiff1 %>% select(matches("H_index"):matches("globEff")) %>% colnames(.)
for(i in 1:length(networkMeasures)){
corsDf <- indDiff1 %>% select(SE:CESD, networkMeasures[i] ) %>% corToOne(., networkMeasures[i])
barsDf <- indDiff1 %>% select(SE:CESD, networkMeasures[i] ) %>% plotCorToOne(., networkMeasures[i])
assign(paste0(networkMeasures[i],".CorDf"),corsDf)
assign(paste0(networkMeasures[i],".CorPlotDf"),barsDf)
print(corsDf)
print(barsDf)
}## Loading required package: tidyverse## ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.0 ──## ✔ tibble 3.1.7 ✔ purrr 0.3.4
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## ✖ tidyr::expand() masks Matrix::expand()
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()
## ✖ tidyr::pack() masks Matrix::pack()
## ✖ tidyr::unpack() masks Matrix::unpack()## Loading required package: corrr##
## Correlation method: 'pearson'
## Missing treated using: 'pairwise.complete.obs'## Note: Using an external vector in selections is ambiguous.
## ℹ Use `all_of(referenceVar)` instead of `referenceVar` to silence this message.
## ℹ See <https://tidyselect.r-lib.org/reference/faq-external-vector.html>.
## This message is displayed once per session.## [1] "All required packages attached"##
## Correlation method: 'pearson'
## Missing treated using: 'pairwise.complete.obs'## # A tibble: 12 × 2
## Variables H_index
## <chr> <dbl>
## 1 SCC -0.0665
## 2 SE -0.0168
## 3 IdImp -0.00887
## 4 SWLS -0.000116
## 5 MemSE 0.00559
## 6 PrivCSE 0.0118
## 7 PubCSE 0.0195
## 8 DS 0.0406
## 9 CESD 0.0855
## 10 NFC 0.127
## 11 Inter 0.148
## 12 Ind 0.190## [1] "All required packages attached"##
## Correlation method: 'pearson'
## Missing treated using: 'pairwise.complete.obs'## [1] "All required packages attached"##
## Correlation method: 'pearson'
## Missing treated using: 'pairwise.complete.obs'
## # A tibble: 12 × 2
## Variables overlap_norm
## <chr> <dbl>
## 1 DS -0.101
## 2 CESD -0.0496
## 3 IdImp 0.0363
## 4 SCC 0.0596
## 5 SE 0.0813
## 6 SWLS 0.0992
## 7 MemSE 0.103
## 8 PrivCSE 0.154
## 9 Inter 0.177
## 10 PubCSE 0.177
## 11 NFC 0.197
## 12 Ind 0.248## [1] "All required packages attached"##
## Correlation method: 'pearson'
## Missing treated using: 'pairwise.complete.obs'## [1] "All required packages attached"##
## Correlation method: 'pearson'
## Missing treated using: 'pairwise.complete.obs'
## # A tibble: 12 × 2
## Variables phi
## <chr> <dbl>
## 1 SWLS -0.218
## 2 NFC -0.153
## 3 SE -0.148
## 4 SCC -0.0798
## 5 MemSE -0.0766
## 6 PubCSE -0.0722
## 7 Ind -0.0528
## 8 Inter -0.0318
## 9 PrivCSE -0.0125
## 10 IdImp 0.0158
## 11 DS 0.0164
## 12 CESD 0.0764## [1] "All required packages attached"##
## Correlation method: 'pearson'
## Missing treated using: 'pairwise.complete.obs'## [1] "All required packages attached"##
## Correlation method: 'pearson'
## Missing treated using: 'pairwise.complete.obs'
## # A tibble: 12 × 2
## Variables idEdgT
## <chr> <dbl>
## 1 SE -0.0571
## 2 Ind -0.0512
## 3 SCC -0.0301
## 4 DS -0.00381
## 5 SWLS 0.00368
## 6 MemSE 0.00383
## 7 Inter 0.0213
## 8 PubCSE 0.0221
## 9 NFC 0.0255
## 10 PrivCSE 0.0328
## 11 IdImp 0.0689
## 12 CESD 0.0844## [1] "All required packages attached"##
## Correlation method: 'pearson'
## Missing treated using: 'pairwise.complete.obs'## [1] "All required packages attached"##
## Correlation method: 'pearson'
## Missing treated using: 'pairwise.complete.obs'
## # A tibble: 12 × 2
## Variables idDensity
## <chr> <dbl>
## 1 SE -0.0571
## 2 Ind -0.0512
## 3 SCC -0.0301
## 4 DS -0.00381
## 5 SWLS 0.00368
## 6 MemSE 0.00383
## 7 Inter 0.0213
## 8 PubCSE 0.0221
## 9 NFC 0.0255
## 10 PrivCSE 0.0328
## 11 IdImp 0.0689
## 12 CESD 0.0844## [1] "All required packages attached"##
## Correlation method: 'pearson'
## Missing treated using: 'pairwise.complete.obs'## [1] "All required packages attached"##
## Correlation method: 'pearson'
## Missing treated using: 'pairwise.complete.obs'
## # A tibble: 12 × 2
## Variables idTrans
## <chr> <dbl>
## 1 SE -0.0547
## 2 DS -0.0443
## 3 PubCSE -0.0417
## 4 Inter -0.0256
## 5 SCC -0.0206
## 6 MemSE -0.0181
## 7 PrivCSE -0.0112
## 8 Ind -0.00978
## 9 SWLS 0.0127
## 10 CESD 0.0551
## 11 IdImp 0.0646
## 12 NFC 0.0687## [1] "All required packages attached"##
## Correlation method: 'pearson'
## Missing treated using: 'pairwise.complete.obs'## [1] "All required packages attached"##
## Correlation method: 'pearson'
## Missing treated using: 'pairwise.complete.obs'
## # A tibble: 12 × 2
## Variables idSW
## <chr> <dbl>
## 1 PubCSE -0.0533
## 2 SE -0.0436
## 3 Inter -0.0207
## 4 SCC -0.0148
## 5 DS -0.0114
## 6 MemSE 0.0306
## 7 CESD 0.0392
## 8 PrivCSE 0.0419
## 9 IdImp 0.0568
## 10 SWLS 0.0685
## 11 Ind 0.0770
## 12 NFC 0.121## [1] "All required packages attached"##
## Correlation method: 'pearson'
## Missing treated using: 'pairwise.complete.obs'## [1] "All required packages attached"##
## Correlation method: 'pearson'
## Missing treated using: 'pairwise.complete.obs'
## # A tibble: 12 × 2
## Variables idStrengHom
## <chr> <dbl>
## 1 SWLS -0.0444
## 2 DS -0.0328
## 3 SCC -0.00631
## 4 IdImp 0.0159
## 5 SE 0.0242
## 6 Inter 0.0466
## 7 NFC 0.0485
## 8 PrivCSE 0.0647
## 9 CESD 0.0837
## 10 PubCSE 0.0998
## 11 MemSE 0.102
## 12 Ind 0.110## [1] "All required packages attached"##
## Correlation method: 'pearson'
## Missing treated using: 'pairwise.complete.obs'## [1] "All required packages attached"##
## Correlation method: 'pearson'
## Missing treated using: 'pairwise.complete.obs'
## # A tibble: 12 × 2
## Variables idPosHom
## <chr> <dbl>
## 1 PubCSE -0.0486
## 2 SCC -0.0443
## 3 PrivCSE -0.0437
## 4 Ind -0.0239
## 5 NFC -0.0149
## 6 MemSE -0.0106
## 7 Inter 0.0226
## 8 SE 0.0246
## 9 IdImp 0.0320
## 10 DS 0.0387
## 11 CESD 0.0608
## 12 SWLS 0.143## [1] "All required packages attached"##
## Correlation method: 'pearson'
## Missing treated using: 'pairwise.complete.obs'## [1] "All required packages attached"##
## Correlation method: 'pearson'
## Missing treated using: 'pairwise.complete.obs'
## # A tibble: 12 × 2
## Variables idUnifHom
## <chr> <dbl>
## 1 SCC -0.110
## 2 PubCSE -0.105
## 3 SE -0.0351
## 4 MemSE 0.00428
## 5 SWLS 0.0544
## 6 Ind 0.0569
## 7 PrivCSE 0.0584
## 8 Inter 0.0847
## 9 NFC 0.0918
## 10 IdImp 0.0927
## 11 DS 0.107
## 12 CESD 0.122## [1] "All required packages attached"##
## Correlation method: 'pearson'
## Missing treated using: 'pairwise.complete.obs'## [1] "All required packages attached"##
## Correlation method: 'pearson'
## Missing treated using: 'pairwise.complete.obs'
## # A tibble: 12 × 2
## Variables idMallHom
## <chr> <dbl>
## 1 PrivCSE -0.0958
## 2 MemSE -0.0810
## 3 SCC -0.0579
## 4 Ind -0.0525
## 5 IdImp -0.0414
## 6 PubCSE -0.0355
## 7 Inter -0.0143
## 8 SE -0.00409
## 9 DS -0.000136
## 10 SWLS 0.0579
## 11 NFC 0.0731
## 12 CESD 0.101## [1] "All required packages attached"##
## Correlation method: 'pearson'
## Missing treated using: 'pairwise.complete.obs'## [1] "All required packages attached"##
## Correlation method: 'pearson'
## Missing treated using: 'pairwise.complete.obs'
## # A tibble: 12 × 2
## Variables idCommGlob
## <chr> <dbl>
## 1 DS -0.0605
## 2 SE -0.0236
## 3 Ind -0.0145
## 4 SCC -0.00177
## 5 PubCSE 0.00298
## 6 MemSE 0.00876
## 7 SWLS 0.0186
## 8 NFC 0.0491
## 9 Inter 0.0500
## 10 PrivCSE 0.0646
## 11 CESD 0.0792
## 12 IdImp 0.0889## [1] "All required packages attached"##
## Correlation method: 'pearson'
## Missing treated using: 'pairwise.complete.obs'## [1] "All required packages attached"##
## Correlation method: 'pearson'
## Missing treated using: 'pairwise.complete.obs'
## # A tibble: 12 × 2
## Variables traitCommGlob
## <chr> <dbl>
## 1 DS -0.0808
## 2 CESD -0.0164
## 3 SCC 0.0330
## 4 IdImp 0.0364
## 5 SE 0.0735
## 6 MemSE 0.0755
## 7 SWLS 0.103
## 8 Inter 0.125
## 9 Ind 0.126
## 10 PrivCSE 0.132
## 11 NFC 0.150
## 12 PubCSE 0.173## [1] "All required packages attached"##
## Correlation method: 'pearson'
## Missing treated using: 'pairwise.complete.obs'## [1] "All required packages attached"##
## Correlation method: 'pearson'
## Missing treated using: 'pairwise.complete.obs'
## # A tibble: 12 × 2
## Variables globEff
## <chr> <dbl>
## 1 SCC -0.0494
## 2 SE -0.0366
## 3 PubCSE -0.0330
## 4 MemSE -0.00756
## 5 DS -0.00580
## 6 Ind -0.00291
## 7 SWLS 0.00570
## 8 PrivCSE 0.0418
## 9 Inter 0.0552
## 10 NFC 0.0748
## 11 IdImp 0.107
## 12 CESD 0.131
Study 2
networkMeasures <- indDiff2 %>% select(matches("H_index"):matches("globEff")) %>% colnames(.)
for(i in 1:length(networkMeasures)){
corsDf <- indDiff2 %>% select(SE:MC, networkMeasures[i] ) %>% corToOne(., networkMeasures[i])
barsDf <- indDiff2 %>% select(SE:MC, networkMeasures[i] ) %>% plotCorToOne(., networkMeasures[i])
assign(paste0(networkMeasures[i],".CorDf"),corsDf)
assign(paste0(networkMeasures[i],".CorPlotDf"),barsDf)
print(corsDf)
print(barsDf)
}## [1] "All required packages attached"##
## Correlation method: 'pearson'
## Missing treated using: 'pairwise.complete.obs'## [1] "All required packages attached"##
## Correlation method: 'pearson'
## Missing treated using: 'pairwise.complete.obs'## # A tibble: 15 × 2
## Variables H_index
## <chr> <dbl>
## 1 MC -0.141
## 2 EMS -0.105
## 3 SCC -0.0928
## 4 SWLS -0.0840
## 5 SE -0.0640
## 6 NFC 0.0349
## 7 Ind 0.0364
## 8 PubCSE 0.0553
## 9 Inter 0.0564
## 10 IdImp 0.0597
## 11 CESD 0.0689
## 12 MemSE 0.0694
## 13 PrivCSE 0.0873
## 14 DS 0.118
## 15 IMS 0.127## [1] "All required packages attached"##
## Correlation method: 'pearson'
## Missing treated using: 'pairwise.complete.obs'## [1] "All required packages attached"##
## Correlation method: 'pearson'
## Missing treated using: 'pairwise.complete.obs'
## # A tibble: 15 × 2
## Variables overlap_norm
## <chr> <dbl>
## 1 EMS -0.126
## 2 MC -0.0700
## 3 SWLS -0.0334
## 4 CESD -0.0127
## 5 SCC -0.0117
## 6 Ind 0.0402
## 7 SE 0.0414
## 8 PubCSE 0.0442
## 9 MemSE 0.0630
## 10 NFC 0.0723
## 11 DS 0.0752
## 12 IdImp 0.0770
## 13 IMS 0.0800
## 14 Inter 0.0821
## 15 PrivCSE 0.122## [1] "All required packages attached"##
## Correlation method: 'pearson'
## Missing treated using: 'pairwise.complete.obs'## [1] "All required packages attached"##
## Correlation method: 'pearson'
## Missing treated using: 'pairwise.complete.obs'
## # A tibble: 15 × 2
## Variables phi
## <chr> <dbl>
## 1 IdImp -0.136
## 2 EMS -0.114
## 3 PrivCSE -0.0621
## 4 SCC -0.0322
## 5 PubCSE -0.0321
## 6 CESD -0.0221
## 7 MemSE -0.0205
## 8 Ind -0.00707
## 9 DS 0.00926
## 10 IMS 0.0112
## 11 MC 0.0162
## 12 Inter 0.0311
## 13 SWLS 0.0413
## 14 NFC 0.0715
## 15 SE 0.100## [1] "All required packages attached"##
## Correlation method: 'pearson'
## Missing treated using: 'pairwise.complete.obs'## [1] "All required packages attached"##
## Correlation method: 'pearson'
## Missing treated using: 'pairwise.complete.obs'
## # A tibble: 15 × 2
## Variables idEdgT
## <chr> <dbl>
## 1 PrivCSE -0.115
## 2 SCC -0.0786
## 3 MemSE -0.0741
## 4 MC -0.0699
## 5 IMS -0.0551
## 6 NFC -0.0444
## 7 Ind -0.0442
## 8 PubCSE -0.0109
## 9 SE -0.00581
## 10 Inter 0.0185
## 11 DS 0.0317
## 12 EMS 0.0400
## 13 SWLS 0.0512
## 14 IdImp 0.0690
## 15 CESD 0.0826## [1] "All required packages attached"##
## Correlation method: 'pearson'
## Missing treated using: 'pairwise.complete.obs'## [1] "All required packages attached"##
## Correlation method: 'pearson'
## Missing treated using: 'pairwise.complete.obs'
## # A tibble: 15 × 2
## Variables idDensity
## <chr> <dbl>
## 1 PrivCSE -0.115
## 2 SCC -0.0786
## 3 MemSE -0.0741
## 4 MC -0.0699
## 5 IMS -0.0551
## 6 NFC -0.0444
## 7 Ind -0.0442
## 8 PubCSE -0.0109
## 9 SE -0.00581
## 10 Inter 0.0185
## 11 DS 0.0317
## 12 EMS 0.0400
## 13 SWLS 0.0512
## 14 IdImp 0.0690
## 15 CESD 0.0826## [1] "All required packages attached"##
## Correlation method: 'pearson'
## Missing treated using: 'pairwise.complete.obs'## [1] "All required packages attached"##
## Correlation method: 'pearson'
## Missing treated using: 'pairwise.complete.obs'
## # A tibble: 15 × 2
## Variables idTrans
## <chr> <dbl>
## 1 IMS -0.106
## 2 MemSE -0.0365
## 3 Ind -0.0294
## 4 PrivCSE -0.0289
## 5 SCC -0.0268
## 6 NFC -0.0128
## 7 MC -0.00445
## 8 PubCSE -0.00337
## 9 Inter 0.00773
## 10 SE 0.0104
## 11 DS 0.0128
## 12 SWLS 0.0570
## 13 CESD 0.0660
## 14 EMS 0.0824
## 15 IdImp 0.111## [1] "All required packages attached"##
## Correlation method: 'pearson'
## Missing treated using: 'pairwise.complete.obs'## [1] "All required packages attached"##
## Correlation method: 'pearson'
## Missing treated using: 'pairwise.complete.obs'
## # A tibble: 15 × 2
## Variables idSW
## <chr> <dbl>
## 1 IMS -0.0970
## 2 PubCSE -0.0521
## 3 CESD -0.0493
## 4 EMS -0.0396
## 5 Inter -0.0152
## 6 DS -0.0117
## 7 SWLS 0.00493
## 8 NFC 0.0137
## 9 PrivCSE 0.0183
## 10 MemSE 0.0214
## 11 SE 0.0296
## 12 MC 0.0428
## 13 IdImp 0.0432
## 14 Ind 0.0557
## 15 SCC 0.102## [1] "All required packages attached"##
## Correlation method: 'pearson'
## Missing treated using: 'pairwise.complete.obs'## [1] "All required packages attached"##
## Correlation method: 'pearson'
## Missing treated using: 'pairwise.complete.obs'
## # A tibble: 15 × 2
## Variables idStrengHom
## <chr> <dbl>
## 1 DS -0.0805
## 2 CESD -0.0704
## 3 MC -0.0518
## 4 NFC -0.0398
## 5 Inter -0.0168
## 6 EMS 0.0142
## 7 Ind 0.0377
## 8 PrivCSE 0.0378
## 9 IdImp 0.0391
## 10 PubCSE 0.0395
## 11 SCC 0.0441
## 12 MemSE 0.0532
## 13 IMS 0.0613
## 14 SE 0.0841
## 15 SWLS 0.129## [1] "All required packages attached"##
## Correlation method: 'pearson'
## Missing treated using: 'pairwise.complete.obs'## [1] "All required packages attached"##
## Correlation method: 'pearson'
## Missing treated using: 'pairwise.complete.obs'
## # A tibble: 15 × 2
## Variables idPosHom
## <chr> <dbl>
## 1 CESD -0.0837
## 2 PubCSE -0.0741
## 3 MemSE -0.0672
## 4 DS -0.0666
## 5 NFC -0.0664
## 6 IMS -0.0495
## 7 MC -0.0384
## 8 Inter -0.0302
## 9 Ind -0.0213
## 10 PrivCSE -0.0114
## 11 IdImp 0.00715
## 12 SCC 0.0285
## 13 SE 0.0674
## 14 EMS 0.0776
## 15 SWLS 0.0826## [1] "All required packages attached"##
## Correlation method: 'pearson'
## Missing treated using: 'pairwise.complete.obs'## [1] "All required packages attached"##
## Correlation method: 'pearson'
## Missing treated using: 'pairwise.complete.obs'
## # A tibble: 15 × 2
## Variables idStigHom
## <chr> <dbl>
## 1 EMS -0.105
## 2 SWLS -0.0594
## 3 PrivCSE -0.0387
## 4 MemSE -0.0387
## 5 IdImp -0.0368
## 6 Ind -0.0290
## 7 DS -0.0119
## 8 PubCSE -0.0114
## 9 Inter -0.0111
## 10 MC -0.0104
## 11 SE 0.00195
## 12 SCC 0.00535
## 13 IMS 0.0323
## 14 CESD 0.0383
## 15 NFC 0.0943## [1] "All required packages attached"##
## Correlation method: 'pearson'
## Missing treated using: 'pairwise.complete.obs'## [1] "All required packages attached"##
## Correlation method: 'pearson'
## Missing treated using: 'pairwise.complete.obs'
## # A tibble: 15 × 2
## Variables idSizeSHom
## <chr> <dbl>
## 1 MC -0.0712
## 2 EMS -0.0625
## 3 Ind -0.0589
## 4 DS -0.0428
## 5 CESD -0.0403
## 6 SWLS -0.0359
## 7 SE -0.0324
## 8 PrivCSE -0.0269
## 9 NFC -0.00678
## 10 Inter 0.0236
## 11 SCC 0.0430
## 12 MemSE 0.0603
## 13 IdImp 0.112
## 14 PubCSE 0.113
## 15 IMS 0.120## [1] "All required packages attached"##
## Correlation method: 'pearson'
## Missing treated using: 'pairwise.complete.obs'## [1] "All required packages attached"##
## Correlation method: 'pearson'
## Missing treated using: 'pairwise.complete.obs'
## # A tibble: 15 × 2
## Variables idSizeUHom
## <chr> <dbl>
## 1 SWLS -0.0964
## 2 EMS -0.0616
## 3 NFC -0.0494
## 4 IdImp -0.0414
## 5 IMS -0.0293
## 6 Inter -0.0226
## 7 MC -0.0136
## 8 Ind -0.00966
## 9 SE -0.00218
## 10 DS 0.00616
## 11 PubCSE 0.0170
## 12 CESD 0.0268
## 13 PrivCSE 0.0384
## 14 SCC 0.0656
## 15 MemSE 0.0813## [1] "All required packages attached"##
## Correlation method: 'pearson'
## Missing treated using: 'pairwise.complete.obs'## [1] "All required packages attached"##
## Correlation method: 'pearson'
## Missing treated using: 'pairwise.complete.obs'
## # A tibble: 15 × 2
## Variables idSizeDHom
## <chr> <dbl>
## 1 MC -0.132
## 2 Ind -0.102
## 3 NFC -0.0959
## 4 CESD -0.0805
## 5 EMS -0.0500
## 6 MemSE -0.0157
## 7 PrivCSE -0.0120
## 8 IdImp 0.0158
## 9 Inter 0.0183
## 10 SWLS 0.0239
## 11 PubCSE 0.0343
## 12 DS 0.0432
## 13 SE 0.0439
## 14 SCC 0.0463
## 15 IMS 0.0621## [1] "All required packages attached"##
## Correlation method: 'pearson'
## Missing treated using: 'pairwise.complete.obs'## [1] "All required packages attached"##
## Correlation method: 'pearson'
## Missing treated using: 'pairwise.complete.obs'
## # A tibble: 15 × 2
## Variables idInclusHom
## <chr> <dbl>
## 1 SE -0.107
## 2 SCC -0.0977
## 3 NFC -0.0933
## 4 EMS -0.0733
## 5 MemSE -0.0284
## 6 MC -0.0119
## 7 IdImp 0.000756
## 8 PrivCSE 0.000878
## 9 DS 0.00376
## 10 Ind 0.0154
## 11 PubCSE 0.0203
## 12 SWLS 0.0472
## 13 CESD 0.0474
## 14 Inter 0.0591
## 15 IMS 0.0734## [1] "All required packages attached"##
## Correlation method: 'pearson'
## Missing treated using: 'pairwise.complete.obs'## [1] "All required packages attached"##
## Correlation method: 'pearson'
## Missing treated using: 'pairwise.complete.obs'
## # A tibble: 15 × 2
## Variables idDifferHom
## <chr> <dbl>
## 1 MemSE -0.164
## 2 MC -0.126
## 3 IMS -0.0976
## 4 SE -0.0863
## 5 Ind -0.0820
## 6 Inter -0.0746
## 7 NFC -0.0653
## 8 PubCSE -0.0638
## 9 PrivCSE -0.0430
## 10 CESD -0.0220
## 11 SWLS -0.00157
## 12 IdImp 0.0144
## 13 EMS 0.0147
## 14 SCC 0.0391
## 15 DS 0.0681## [1] "All required packages attached"##
## Correlation method: 'pearson'
## Missing treated using: 'pairwise.complete.obs'## [1] "All required packages attached"##
## Correlation method: 'pearson'
## Missing treated using: 'pairwise.complete.obs'
## # A tibble: 15 × 2
## Variables idInbHom
## <chr> <dbl>
## 1 SE -0.128
## 2 MC -0.122
## 3 SCC -0.0657
## 4 Ind -0.0657
## 5 MemSE -0.0405
## 6 NFC -0.0377
## 7 DS -0.0210
## 8 Inter -0.00675
## 9 EMS 0.00403
## 10 SWLS 0.0151
## 11 IMS 0.0341
## 12 PrivCSE 0.0437
## 13 PubCSE 0.0592
## 14 CESD 0.0598
## 15 IdImp 0.0676## [1] "All required packages attached"##
## Correlation method: 'pearson'
## Missing treated using: 'pairwise.complete.obs'## [1] "All required packages attached"##
## Correlation method: 'pearson'
## Missing treated using: 'pairwise.complete.obs'
## # A tibble: 15 × 2
## Variables idOutbHom
## <chr> <dbl>
## 1 IdImp -0.116
## 2 MemSE -0.104
## 3 SCC -0.0885
## 4 PrivCSE -0.0857
## 5 PubCSE -0.0339
## 6 Ind -0.0238
## 7 MC -0.0202
## 8 CESD -0.0170
## 9 IMS -0.00583
## 10 SE -0.00181
## 11 Inter -0.00169
## 12 NFC -0.000641
## 13 EMS 0.0435
## 14 SWLS 0.0494
## 15 DS 0.104## [1] "All required packages attached"##
## Correlation method: 'pearson'
## Missing treated using: 'pairwise.complete.obs'## [1] "All required packages attached"##
## Correlation method: 'pearson'
## Missing treated using: 'pairwise.complete.obs'
## # A tibble: 15 × 2
## Variables idInterbHom
## <chr> <dbl>
## 1 MemSE -0.178
## 2 MC -0.122
## 3 PrivCSE -0.121
## 4 IdImp -0.0729
## 5 SCC -0.0723
## 6 SE -0.0644
## 7 Ind -0.0321
## 8 SWLS -0.0126
## 9 EMS 0.00285
## 10 IMS 0.0120
## 11 PubCSE 0.0301
## 12 NFC 0.0315
## 13 CESD 0.0439
## 14 Inter 0.0561
## 15 DS 0.0690## [1] "All required packages attached"##
## Correlation method: 'pearson'
## Missing treated using: 'pairwise.complete.obs'## [1] "All required packages attached"##
## Correlation method: 'pearson'
## Missing treated using: 'pairwise.complete.obs'
## # A tibble: 15 × 2
## Variables traitCommGlob
## <chr> <dbl>
## 1 MC -0.0630
## 2 IMS -0.0195
## 3 SWLS -0.0179
## 4 Ind -0.0135
## 5 SCC -0.00824
## 6 EMS -0.00396
## 7 PubCSE 0.00558
## 8 MemSE 0.0171
## 9 NFC 0.0222
## 10 PrivCSE 0.0240
## 11 CESD 0.0321
## 12 Inter 0.0338
## 13 SE 0.0395
## 14 DS 0.0773
## 15 IdImp 0.112## [1] "All required packages attached"##
## Correlation method: 'pearson'
## Missing treated using: 'pairwise.complete.obs'## [1] "All required packages attached"##
## Correlation method: 'pearson'
## Missing treated using: 'pairwise.complete.obs'
## # A tibble: 15 × 2
## Variables idCommGlob
## <chr> <dbl>
## 1 PrivCSE -0.134
## 2 IMS -0.129
## 3 MemSE -0.0921
## 4 SCC -0.0725
## 5 MC -0.0593
## 6 Ind -0.0554
## 7 NFC -0.0518
## 8 PubCSE -0.0265
## 9 SE -0.00974
## 10 Inter -0.00145
## 11 SWLS 0.0482
## 12 IdImp 0.0508
## 13 DS 0.0559
## 14 CESD 0.104
## 15 EMS 0.107## [1] "All required packages attached"##
## Correlation method: 'pearson'
## Missing treated using: 'pairwise.complete.obs'## [1] "All required packages attached"##
## Correlation method: 'pearson'
## Missing treated using: 'pairwise.complete.obs'
## # A tibble: 15 × 2
## Variables globEff
## <chr> <dbl>
## 1 MC -0.126
## 2 PrivCSE -0.124
## 3 IMS -0.120
## 4 SCC -0.116
## 5 MemSE -0.104
## 6 NFC -0.0686
## 7 SE -0.0523
## 8 PubCSE -0.0465
## 9 Ind -0.0334
## 10 Inter 0.0203
## 11 SWLS 0.0347
## 12 DS 0.0629
## 13 IdImp 0.0667
## 14 EMS 0.128
## 15 CESD 0.153
Normative Latino Ratings
normTraits <- read.csv("~/Google Drive/Volumes/Research Project/Identities to Traits/Study 2/Cleaning/Output/Normative/normativeDfStudy12.csv", header = T)
fullLong1s <- fullLong1 %>% select(subID,id,idC,connect,traits,selfResp,IdIn,connect,streng)
fullLong2s <- fullLong2 %>% select(subID,id,idC,connect,traits,selfResp,IdIn,connect,streng)
fullLong2s$subID <- fullLong2$subID + 100000
combLong <- rbind(fullLong1s,fullLong2s)
combLong <- merge(combLong, normTraits, by = "traits")
combLong$ynLatin <- ifelse(combLong$idC==2 & combLong$id=="Race", "HL", "Not HL")
combLong$ynAsian <- ifelse(combLong$idC==4 & combLong$id=="Race", "As", "Not As")
combLong$ynMale <- ifelse(combLong$idC==1 & combLong$id=="Gen", "M", "Not M")
combLong$ynFemale <- ifelse(combLong$idC==2 & combLong$id=="Gen", "F", "Not F")
combLong$ynHetero <- ifelse(combLong$idC==1 & combLong$id=="Sex", "Het", "Not Het")
combLong$ynBis <- ifelse(combLong$idC==3 & combLong$id=="Sex", "Bi", "Not Bi")
combLong$ynCath <- ifelse(combLong$idC==1 & combLong$id=="Rel", "Cath", "Not Cath")
combLong$ynChrist <- ifelse(combLong$idC==2 & combLong$id=="Rel", "Christ", "Not Christ")
combLong$ynAgnos <- ifelse(combLong$idC==8 & combLong$id=="Rel", "Agn", "Not Agn")
combLong$ynAthei <- ifelse(combLong$idC==9 & combLong$id=="Rel", "Ath", "Not Ath")
combLong$ynDem <- ifelse(combLong$idC==1 & combLong$id=="Pol", "Dem", "Not Dem")raceLong <- subset(combLong, id == "Race")
m <- lmer( scale(selfResp) ~ scale(Latino)*ynLatin + ( scale(Latino) | subID) + ( 1 | traits), data = raceLong)## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
## Model failed to converge with max|grad| = 0.00279822 (tol = 0.002, component 1)summary(m)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(selfResp) ~ scale(Latino) * ynLatin + (scale(Latino) |
## subID) + (1 | traits)
## Data: raceLong
##
## REML criterion at convergence: 349190.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.8241 -0.6639 -0.0196 0.6543 4.1272
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subID (Intercept) 0.04726 0.2174
## scale(Latino) 0.03144 0.1773 -0.13
## traits (Intercept) 0.22170 0.4708
## Residual 0.61602 0.7849
## Number of obs: 146472, groups: subID, 495; traits, 296
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -0.03636 0.03240 521.62068 -1.122 0.26234
## scale(Latino) 0.32761 0.03088 451.25397 10.610 < 2e-16 ***
## ynLatinNot HL 0.05416 0.02121 492.98224 2.553 0.01098 *
## scale(Latino):ynLatinNot HL -0.05624 0.01748 492.92993 -3.217 0.00138 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) scl(L) ynLNHL
## scale(Latn) -0.030
## ynLatinNtHL -0.438 0.045
## scl(L):LNHL 0.052 -0.379 -0.120
## optimizer (nloptwrap) convergence code: 0 (OK)
## Model failed to converge with max|grad| = 0.00279822 (tol = 0.002, component 1)ggpredict(m, c("Latino", "ynLatin")) %>% plot()
m <- lmer( scale(selfResp) ~ scale(Asian)*ynAsian + ( scale(Asian) | subID) + ( 1 | traits), data = raceLong)## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
## Model failed to converge with max|grad| = 0.00221085 (tol = 0.002, component 1)summary(m)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(selfResp) ~ scale(Asian) * ynAsian + (scale(Asian) | subID) +
## (1 | traits)
## Data: raceLong
##
## REML criterion at convergence: 348652.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -5.1844 -0.6590 -0.0212 0.6499 4.6725
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subID (Intercept) 0.04782 0.2187
## scale(Asian) 0.03129 0.1769 -0.20
## traits (Intercept) 0.20100 0.4483
## Residual 0.61389 0.7835
## Number of obs: 146472, groups: subID, 495; traits, 296
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 0.01214 0.03064 511.00512 0.396 0.692
## scale(Asian) 0.25521 0.02920 440.93320 8.739 < 2e-16 ***
## ynAsianNot As -0.02005 0.02060 492.96482 -0.973 0.331
## scale(Asian):ynAsianNot As 0.11168 0.01685 492.97887 6.629 8.92e-11 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) scl(A) ynAsNA
## scale(Asin) -0.045
## ynAsianNtAs -0.412 0.068
## scl(As):ANA 0.079 -0.353 -0.191
## optimizer (nloptwrap) convergence code: 0 (OK)
## Model failed to converge with max|grad| = 0.00221085 (tol = 0.002, component 1)ggpredict(m, c("Asian", "ynAsian")) %>% plot()
m <- lmer( scale(selfResp) ~ scale(Asian)*ynAsian*streng + ( scale(Asian) | subID) + ( 1 | traits), data = raceLong)## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
## Model failed to converge with max|grad| = 0.00416783 (tol = 0.002, component 1)summary(m)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(selfResp) ~ scale(Asian) * ynAsian * streng + (scale(Asian) |
## subID) + (1 | traits)
## Data: raceLong
##
## REML criterion at convergence: 348638.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -5.1822 -0.6591 -0.0213 0.6496 4.6485
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subID (Intercept) 0.04754 0.2180
## scale(Asian) 0.02915 0.1707 -0.20
## traits (Intercept) 0.20097 0.4483
## Residual 0.61389 0.7835
## Number of obs: 146472, groups: subID, 495; traits, 296
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 0.26323 0.12290 536.27489 2.142
## scale(Asian) -0.01058 0.09878 561.73559 -0.107
## ynAsianNot As -0.33284 0.16712 490.98978 -1.992
## streng -0.03752 0.01778 490.97799 -2.110
## scale(Asian):ynAsianNot As -0.10563 0.13258 490.90766 -0.797
## scale(Asian):streng 0.03971 0.01411 490.88169 2.815
## ynAsianNot As:streng 0.04669 0.02472 490.98999 1.889
## scale(Asian):ynAsianNot As:streng 0.03211 0.01961 490.90845 1.637
## Pr(>|t|)
## (Intercept) 0.03266 *
## scale(Asian) 0.91478
## ynAsianNot As 0.04697 *
## streng 0.03540 *
## scale(Asian):ynAsianNot As 0.42603
## scale(Asian):streng 0.00508 **
## ynAsianNot As:streng 0.05953 .
## scale(Asian):ynAsianNot As:streng 0.10222
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) scl(A) ynAsNA streng sc(A):ANA sc(A): ynANA:
## scale(Asin) -0.182
## ynAsianNtAs -0.702 0.134
## streng -0.968 0.184 0.712
## scl(As):ANA 0.136 -0.693 -0.193 -0.137
## scl(Asn):st 0.187 -0.956 -0.137 -0.193 0.712
## ynAsnNtAs:s 0.697 -0.133 -0.992 -0.719 0.191 0.139
## scl(A):ANA: -0.134 0.688 0.191 0.139 -0.992 -0.719 -0.193
## optimizer (nloptwrap) convergence code: 0 (OK)
## Model failed to converge with max|grad| = 0.00416783 (tol = 0.002, component 1)ggpredict(m, c("Asian", "streng")) %>% plot()
GenLong <- subset(combLong, id=="Gen")
m <- lmer( scale(selfResp) ~ scale(Female)*ynFemale + ( scale(Female) | subID) + ( 1 | traits), data = GenLong)## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
## Model failed to converge with max|grad| = 0.00218099 (tol = 0.002, component 1)summary(m)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: scale(selfResp) ~ scale(Female) * ynFemale + (scale(Female) |
## subID) + (1 | traits)
## Data: GenLong
##
## REML criterion at convergence: 344504.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.8983 -0.6503 -0.0183 0.6421 4.3392
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subID (Intercept) 0.04796 0.2190
## scale(Female) 0.04941 0.2223 -0.23
## traits (Intercept) 0.14363 0.3790
## Residual 0.59616 0.7721
## Number of obs: 146472, groups: subID, 495; traits, 296
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -0.002218 0.025542 488.541227 -0.087 0.931
## scale(Female) 0.442976 0.025637 493.917911 17.279 < 2e-16
## ynFemaleNot F 0.005273 0.020546 493.014571 0.257 0.798
## scale(Female):ynFemaleNot F -0.103148 0.020841 493.025884 -4.949 1.02e-06
##
## (Intercept)
## scale(Female) ***
## ynFemaleNot F
## scale(Female):ynFemaleNot F ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) scl(F) ynFmNF
## scale(Feml) -0.058
## ynFemaleNtF -0.319 0.073
## scl(Fm):FNF 0.072 -0.322 -0.225
## optimizer (nloptwrap) convergence code: 0 (OK)
## Model failed to converge with max|grad| = 0.00218099 (tol = 0.002, component 1)ggpredict(m, c("Female", "ynFemale")) %>% plot()
m <- lmer( scale(selfResp) ~ scale(Female)*ynFemale*streng + ( scale(Female) | subID) + ( 1 | traits), data = GenLong)
summary(m)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula:
## scale(selfResp) ~ scale(Female) * ynFemale * streng + (scale(Female) |
## subID) + (1 | traits)
## Data: GenLong
##
## REML criterion at convergence: 344490.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.8971 -0.6504 -0.0184 0.6422 4.3379
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subID (Intercept) 0.04738 0.2177
## scale(Female) 0.04604 0.2146 -0.21
## traits (Intercept) 0.14360 0.3789
## Residual 0.59616 0.7721
## Number of obs: 146472, groups: subID, 495; traits, 296
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 0.36880 0.17061 507.46777 2.162
## scale(Female) -0.32936 0.16831 508.03267 -1.957
## ynFemaleNot F -0.12741 0.21774 490.92729 -0.585
## streng -0.05401 0.02456 490.93140 -2.199
## scale(Female):ynFemaleNot F 0.14801 0.21476 491.02500 0.689
## scale(Female):streng 0.11243 0.02422 491.03362 4.642
## ynFemaleNot F:streng 0.01859 0.03182 490.92751 0.584
## scale(Female):ynFemaleNot F:streng -0.03498 0.03138 491.02512 -1.115
## Pr(>|t|)
## (Intercept) 0.0311 *
## scale(Female) 0.0509 .
## ynFemaleNot F 0.5587
## streng 0.0283 *
## scale(Female):ynFemaleNot F 0.4910
## scale(Female):streng 4.43e-06 ***
## ynFemaleNot F:streng 0.5592
## scale(Female):ynFemaleNot F:streng 0.2655
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) scl(F) ynFmNF streng sc(F):FNF sc(F): ynFNF:
## scale(Feml) -0.198
## ynFemaleNtF -0.770 0.155
## streng -0.989 0.199 0.775
## scl(Fm):FNF 0.155 -0.770 -0.201 -0.156
## scl(Fml):st 0.199 -0.989 -0.156 -0.201 0.775
## ynFmlNtF:st 0.763 -0.153 -0.996 -0.772 0.200 0.155
## scl(F):FNF: -0.153 0.763 0.200 0.155 -0.996 -0.772 -0.201ggpredict(m, c("Female", "ynFemale")) %>% plot()